THE EFFECT OF THERMAL ENVIRONMENT ON SALMONELLA SHEDDING IN
FINISHING PIGS

By

Alda Francelina de Andrade e Pires

A DISSERTATION
Submitted to
Michigan State University
In partial fulfillment of the requirements
For the degree of
DOCTOR OF PHILOSOPHY
Large Animal Clinical Sciences
2012

ABSTRACT
THE EFFECT OF THERMAL ENVIRONMENT ON SALMONELLA SHEDDING IN
FINISHING PIGS
By
Alda Francelina de Andrade e Pires
Salmonella species are one of the major causes of foodborne diseases in the US and
worldwide. The objectives of this dissertation were to describe the shedding pattern of
Salmonella in feces of naturally infected finishing pigs, to compare direct q PCR detection of
Salmonella in swine feces to the microbiological culture, to quantify the fecal concentration of
Salmonella in naturally infected pigs, to evaluate the association between the environmental
thermal parameters in the barn and Salmonella shedding in finishing pigs, and to estimate the
proportion of total model variance attributable to cohort, pig and individual sample level effects
when predicting Salmonella shedding in swine.
A 3 year longitudinal study was conducted on 3 sites of a multi-site farrow to finish
production system. Individual fecal samples from 900 finishing pigs (8 collections per pig) were
repeatedly collected from 18 cohorts (50 pigs per cohort). Fecal samples were collected every 2
weeks for 16 weeks. Salmonella was cultured from 453 (6.6%) of 6836 fecal samples.
Individual fecal samples (positive (n=443), negative (n=1225) determined by microbiological
culture) were submitted for q PCR. Pen temperature and humidity were measured every 2
minutes during the study period. The thermal parameters of interest were: hourly average,
minimum and maximum lagged temperatures, hourly temperature variation, temperature
humidity index (THI) and cumulative number of hours/degree above and below the thermal of
neutral zone at the pen level prior to fecal sampling for 6 time periods (12h, 24h, 48h, 72h, 1

week and 1 month). The pig level incidence of Salmonella was 20.8% (187/899 pigs).
Salmonella prevalence varied between both sites and cohorts within sites. The proportion of
positive samples decreased over the finishing period from 12.9% to 2.8%. Intermittent detection
of Salmonella was found in more than 50% of pigs that were positive at more than one
collection. The finding that the majority of pigs shed intermittently has implications for
surveillance and research study design when determining Salmonella status.
For culture positive samples, 15.4% (68/443) were detected by q PCR, but only 3.4%
3

(15/443) were within the q PCR quantifiable range (≥ 10 CFU/g of feces). Of these latter
3

6

samples, the concentration range was 1.06x10 1.73x10 CFU/g feces. When high shedding was
detected it was clustered within a single pig and its pen mates. Direct q PCR may be an
alternative to traditional culture dependent methods for detection of pigs with high fecal
concentrations of Salmonella, but not for detection of pigs shedding low concentrations.
Multilevel logistic models using generalized linear models, with random intercepts at pig,
pen and cohort levels to account for clustering were constructed. The outcome variable was
Salmonella fecal status of the individual sample. Cold exposure (temperatures below the thermal
neutral zone) and exposure to a THI >72 were both associated with risk of Salmonella shedding.
Nursery Salmonella status, site, pig age and cohort mortality rate were also associated with
Salmonella shedding. The largest proportion of model variance was associated with the
individual fecal sample (44.7%) followed by cohort (24.1%) and pen (20.7%). Interventions that
target the thermal environment may have an effect on reducing Salmonella shedding in swine
and also improve pig well being and production efficiency. Alternatively, thermal parameters
may be used to identify groups of pigs at high risk for Salmonella shedding.

Copyright by
ALDA FRANCELINA DE ANDRADE E PIRES

DEDICATION

This thesis is dedicated to my brother, Jose A. A. Pires, who has been my ‘life coach’, for
always being there for me and for his encouragements.

v

ACKNOWLEDGMENTS
This PhD dissertation has been the result of the contributions of many people without
whom I would not be able to accomplish it. I would like to thank all the contributions to this
project.
First and foremost, I would like to express my gratitude to my major Professor Dr. Julie
Funk for her advice, mentorship and support. It has been an honor to be her PhD student; her
guidance and encouragements kept me on track throughout the graduate school years.
I am grateful for the member of my thesis committee, Dr. Paul Bartlett, and Dr. Carol
Bolin and Dr. Tapabrata Maiti for assisting, guiding, directing, and supporting me through my
field work, data analysis, and writing process of this thesis. I would like to thank my external
reviewer Dr. H. Morgan Scott for his valuable suggestions in the final stages of the dissertation.
I want to thank the staff and technicians of the DCPAH Virology and Bacteriology
section, namely, Ailam Lim, Michael Garrod, Michelle Brown, Nicole Grosjean and Dr. Steve
Bolin, for their technical assistance in laboratory work.
I would like to thank our collaborators from The Ohio State University, Dr. Lingyng
Zhao, Roderick Manuzon and undergraduate students (Natasha Pereira and Pamela Faze), for
their contribution to this research project, namely on the engineering part, collection and
processing of the environmental data.
My special thanks to all the undergraduate and veterinary students (Alexa Buckley, Allan
Mergener, Erin Shaw, Jackie Rowely, Jeremy Shaba, Jessica Seate, Joel Sparks, Joseph Sullivan,
Kenneth Rogers, Maxwell Guta, Nicholas Klosterman, Nina Duflo, Stacia Belda, Tasha Likavek)
vi

and to my colleague, Dr. Marion Tseng for their efforts and time in helping and assisting me in
the field and laboratory work, and data validation of this project. Thank you for your friendship
and for the fond memories of the times at the farms and in the laboratory.
I would like to thank the AjBoggs team, namely Catherine Villaire, Clarke Anderson,
Jacob Hang, Jason Rigdon and Kevin Wang, for their help in database development and
processing. I would like to extend a word of appreciation to Steven Pierce from CSTAT, MSU
for his help in the early stages of database development. I would like to thank the Graduate
Writing Group from the Writing Center at MSU, namely Ben Goodwin, Camie Augustus ,
Christian Hanna, Kaliamma Ponnan, for their valuable suggestions during the writing of this
dissertation.
I gratefully acknowledge the funding of this project from the USDA, NRI Epidemiologic
Approaches to Food Safety # 2007 – 1775. I express my appreciation to the College of
Veterinary Medicine at MSU for the Completion Graduate Fellowship.
I would like to thank the participant producers and staff for having allowed conducting this
research.
I would like to thank my fellow Ph.D. colleagues and friends, Chau Nguyen, Chieh Chien,
Maria Cristina VenegasVargas, Marion Tseng, Mary Joy Gordoncillo, Michelle Lau, Karina
Garcia-Ruano and Zahid Butt for their friendship, support and encouragement during these years
in graduate school.
Lastly, I would like to thank my parents, sisters and brother for all their support and
encouragement; in particular my mom who always believed that a better education for her kids
would bring a better future; my bother for always being there when I needed him.
vii

TABLE OF CONTENTS
LIST OF TABLES ........................................................................................................................ xii
LIST OF FIGURES ..................................................................................................................... xiii
ABBREVIATIONS .................................................................................................................... xvii
INTRODUCTION .......................................................................................................................... 1
REFERENCES ................................................................................................................... 8
CHAPTER 1.Literature Review ................................................................................................... 14
SALMONELLA TAXONOMY ............................................................................. 15
IMPORTANCE OF SALMONELLA IN PUBLIC HEALTH ............................... 16
PATHOGENESIS OF SALMONELLA IN SWINE .............................................. 19
Clinical and subclinical syndromes .......................................................... 19
Sources of infection .................................................................................. 20
Transmission, dose and serovars............................................................... 21
Carriers and intermittent shedders ............................................................ 24
Seroconversion .......................................................................................... 25
DIAGNOSIS OF SALMONELLA IN SWINE .................................................... 26
Serology .................................................................................................... 27
General Salmonella culture ....................................................................... 28
Salmonella fecal culture ............................................................................ 28
Fecal culture protocols and test performance ........................................... 31
Consequences of imperfect sensitivity of fecal culture ............................ 34
Pooled versus individual fecal samples .................................................... 34
Quantification of Salmonella .................................................................... 37
Techniques available to quantify: MPN, MRSV, real time PCR ............ 38
Quantitative real time PCR ...................................................................... 40
Enrichment and no enrichment prior to real time PCR ........................... 41
Detection limit of real time PCR ............................................................. 43
EPIDEMIOLOGY OF SALMONELLA IN SWINE ........................................... 46
Prevalence of Salmonella in swine in the US ........................................... 46
viii

Cross sectional versus longitudinal studies ............................................. 49
Longitudinal studies at the pig level ......................................................... 51
Herd risk factors ........................................................................................ 54
SEASON, ENVIRONMENT FACTORS and FOODBORNE PATHOGENS .... 63
Seasonal pattern of diseases ...................................................................... 63
Seasonality of foodborne pathogens in Humans ....................................... 64
Seasonality of salmonellosis in swine....................................................... 66
Thermal environment factors associated with salmonellosis in swine ..... 68
Thermal neutral zone and thermal stress .................................................. 69
Effects of thermal stress in swine ............................................................. 74
APPENDIX ....................................................................................................................... 82
REFERENCES ................................................................................................................. 86
CHAPTER 2.Longitudinal study of Salmonella shedding in naturally infected finishing pigs . 112
ABSTRACT .................................................................................................................... 113
INTRODUCTION .......................................................................................................... 114
MATERIALS AND METHODS .................................................................................... 116
Sample collection ................................................................................................ 117
Nursery sampling .................................................................................... 117
Environmental sampling ......................................................................... 117
Individual fecal sampling ........................................................................ 118
Bacteriological culture ........................................................................................ 119
Fecal samples .......................................................................................... 119
Environmental samples ........................................................................... 120
Data analysis ........................................................................................... 120
RESULTS ....................................................................................................................... 122
Demographic results ........................................................................................... 122
Nursery and barn environment............................................................................ 122
Site, cohort and age apparent prevalence ............................................................ 123
Pig apparent prevalence and duration of shedding ............................................. 125
DISCUSSION ................................................................................................................. 127
ACKNOWLEDGMENTS .............................................................................................. 132
ix

APPENDIX ..................................................................................................................... 133
REFERENCES ............................................................................................................... 140
CHAPTER 3.Direct quantitative real time PCR for enumeration of Salmonella in feces of
naturally infected pigs ................................................................................................................. 145
ABSTRACT .................................................................................................................... 146
INTRODUCTION .......................................................................................................... 147
MATERIALS AND METHODS .................................................................................... 150
RESULTS ....................................................................................................................... 154
DISCUSSION ................................................................................................................. 156
ACKNOWLEDGMENTS .............................................................................................. 161
APPENDIX ..................................................................................................................... 162
REFERENCES ............................................................................................................... 166
CHAPTER 4.Multilevel analysis to evaluate the association between environmental thermal
parameters and Salmonella shedding in finishing pigs ............................................................... 172
ABSTRACT .................................................................................................................... 173
INTRODUCTION .......................................................................................................... 175
MATERIALS AND METHODS .................................................................................... 178
Study design ........................................................................................................ 178
Sample size ......................................................................................................... 179
Sampling of individual fecal samples ................................................................. 180
Laboratory protocol for isolation of Salmonella ................................................. 180
Environmental data collection and description of barn ventilation systems ....... 181
Environmental thermal parameters ..................................................................... 183
Description of other variables ............................................................................. 184
Software used for data base management and statistical analyses ..................... 185
Data management: exclusions and validation of data ............................. 185
Model building ........................................................................................ 186
Variance components .............................................................................. 188
RESULTS ....................................................................................................................... 189
Assessing linearity of continuous variables and transformation of variables ..... 189
x

Descriptive Results ............................................................................................. 190
Salmonella prevalence results/ isolation of Salmonella.......................... 190
Descriptive statistics of environmental thermal parameters ................... 191
Risk analyses ....................................................................................................... 192
Variance components .......................................................................................... 193
DISCUSSION ................................................................................................................. 194
CONCLUSION ............................................................................................................... 205
ACKNOWLEDGMENTS .............................................................................................. 206
APPENDIX ..................................................................................................................... 207
REFERENCES ............................................................................................................... 240

xi

LIST OF TABLES
Table 1.1. Upper and lower critical temperature criteria of thermal neutral zone of finishing pigs
used to assess the thermal (heat and cold) exposure……………………………...……………...83
Table 2.1. Proportion of samples positive for Salmonella spp by site and cohort (samples
represent individual fecal samples, pooled fecal samples from the source nursery and barn
environmental swabs) and respective 95% confidence intervals…………………………….....134
Table 2.2. Distribution of cohorts and proportion of samples positive for Salmonella spp by the
Salmonella status of nursery and environmental swabs………………………………..………135
Table 4.1. Upper and lower critical temperature criteria of thermal neutral zone of finishing pigs
used to assess the thermal (heat and cold) exposure………………………………………..…..208
Table 4. 2. Proportion of Salmonella positive samples stratified by pig, pen and cohort variables
(risk factors) among 6787 individual pig fecal samples. Multilevel univariable
analysis………………………………………………………………………………………….209
Table 4.3. Descriptive statistics of the continuous thermal environment risk factors, univariable
analysis………………………………………………...………………………………………..211
Table 4.4. Descriptive statistics of the categorical thermal environment risk factors, univariable
analysis. ........................................................................................................................................213
Table 4.5. Final multivariable random effects logistic regression models of associations between
thermal environment parameters, pig level and cohort level risk factors and Salmonella
shedding in finishing pigs in three sites..………………………………….................................218
Table 4.6. Variance components and proportion of variance at the cohort , pen , pig and
individual fecal level of the null model and final model (model 1, cold exposure at 12
hours)...........................................................................................................................................221

xii

LIST OF FIGURES
Figure 1.1. Responses of swine to potential environmental stressors that can have an effect on
production, immunity and animal health………………………………..……………………….84
Figure 1.2. Potential casual pathway of the effect of suboptimal thermal environment on
Salmonella shedding in swine……………………………………………………………………85
Figure 2.1. Box plot representing the distribution of Salmonella positive fecal samples within
each cohort by pig age…………………..……………………………………………………...136
Figure 2.2.a. Apparent prevalence (individual fecal samples) for each cohort (C1 C6) by pig age
in site A..………………………………………………………………………………………..137
Figure 2.2.b. Apparent prevalence (individual fecal samples) for each cohort (C1 C6) by pig age
in site B………………………………………………………………………..……………..…138
Figure 2.2.c. Apparent prevalence (individual fecal samples) for each cohort (C1 C6) by pig age
in site C. Error bars represent 95% exact confidence intervals for proportions………………..139
Figure 3.1. Receiver operating characteristic curve (ROC) for the real time PCR to detect
Salmonella in 1668 pig fecal samples ………………………………………………………….163
Figure 3.2. Distribution of 1668 pig fecal samples classified in scores, based on culture and
direct quantitative real time PCR ……………………………………………………………...164
Figure 3.3. Concentration of Salmonella invA genes in fecal samples of 9 pigs, belonging to
score 3 …….……………………………………………………………………………………165
Figure 4.1.a. Box plot of the average hourly pen temperature ( C) within 24 hours for cohort 1
and 2 by pig age in site A.….……………………………….………..…………………………222
Figure 4.1.b. Box plot of the average hourly pen temperature ( C) within 24 hours for cohort 3
and 4 by pig age in site A……………………………………………………………………….222
Figure 4.1.c. Box plot of the average hourly pen temperature ( C) within 24 hours for cohort 5
and 6 by pig age in site A.….……………………………….………..…………………………223
Figure 4.2.a. Box plot of the average hourly pen temperature ( C) within 24 hours for cohort 1
and 2 by pig age in site B……………………………………………………………………….223

xiii

Figure 4.2.b. Box plot of the average hourly pen temperature ( C) within 24 hours for cohort 3
and 4 by pig age in site B……………………………………………………………………….224
Figure 4.2.c. Box plot of the average hourly pen temperature ( C) within 24 hours for cohort 5
and 6 by pig age in site B……………………………………………………………………….224
Figure 4.3.a. Box plot of the average hourly pen temperature ( C) within 24 hours for cohort 1
and 2 by pig age in site C……………………………………………………………………….225
Figure 4.3.b. Box plot of the average hourly pen temperature ( C) within 24 hours for cohort 3
and 4 by pig age in site C……………………………………………………………………….225
Figure 4.3.c. Box plot of the average hourly pen temperature ( C) within 24 hours for cohort 5
and 6 by pig age in site C……………………………………………………………………….226
Figure 4.4.a. Box plot of the maximum lagged pen temperature ( C) within 24 hours for cohort 1
and 2 by pig age in site A………………………………...………….………………………….226
Figure 4.4.b. Box plot of the maximum lagged pen temperature ( C) within 24 hours for cohort 3
and 4 by pig age in site A……...…………………………………….………………………….227
Figure 4.4.c. Box plot of the maximum lagged pen temperature ( C) within 24 hours for cohort 5
and 6 by pig age in site A……………...…………………………….………………………….227
Figure 4.5.a. Box plot of the maximum lagged pen temperature ( C) within 24 hours for cohort 1
and 2 by pig age in site B………...………………………………….………………………….228
Figure 4.5.b. Box plot of the maximum lagged pen temperature ( C) within 24 hours for cohort 3
and 4 by pig age in site B…………...……………………………….………………………….228
Figure 4.5.c. Box plot of the maximum lagged pen temperature ( C) within 24 hours for cohort 5
and 6 by pig age in site B……………...…………………………….………………………….229
Figure 4.6.a. Box plot of the maximum lagged pen temperature ( C) within 24 hours for cohort 1
and 2 by pig age in site C…………...……………………………….………………………….229
Figure 4.6.b. Box plot of the maximum lagged pen temperature ( C) within 24 hours for cohort 3
and 4 by pig age in site C………………………………………….………………………...….230

xiv

Figure 4.6.c. Box plot of the maximum lagged pen temperature ( C) within 24 hours for cohort 5
and 6 by pig age in site C……...…………………………………….………………………….230
Figure 4.7.a. Box plot of the minimum lagged pen temperature ( C) within 24 hours for cohort 1
and 2 by pig age in site A……………………………………………………………….………231
Figure 4.7.b. Box plot of the minimum lagged pen temperature ( C) within 24 hours for cohort 3
and 4 by pig age in site A…………………………………………………………………….…231
Figure 4.7.c. Box plot of the minimum lagged pen temperature ( C) within 24 hours for cohort 5
and 6 by pig age in site A…………………………………………………………………….…232
Figure 4.8.a. Box plot of the minimum lagged pen temperature ( C) within 24 hours for cohort 1
and 2 by pig age in site B…………………………………………………………………….…232
Figure 4.8.b. Box plot of the minimum lagged pen temperature ( C) within 24 hours for cohort 3
and 4 by pig age in site B……………………...………………………………………….….…233
Figure 4.8.c. Box plot of the minimum lagged pen temperature ( C) within 24 hours for cohort 5
and 6 by pig age in site B…………………………………………………………………….…233
Figure 4.9.a. Box plot of the minimum lagged pen temperature ( C) within 24 hours for cohort 1
and 2 by pig age in site C…………………………………………………………………….…234
Figure 4.9.b. Box plot of the minimum lagged pen temperature ( C) within 24 hours for cohort 3
and 4 by pig age in site C…………………………………………………………………….…234
Figure 4.9.c. Box plot of the minimum lagged pen temperature ( C) within 24 hours for cohort 5
and 6 by pig age in site C…………………………………………………………………….…235
Figure 4.10.a. Box plot of the pen temperature humidity index (THI) index within 24 hours for
cohort 1 and 2 by pig age in site A……………………………….…………………………….235
Figure 4.10.b. Box plot of the pen temperature humidity index (THI) index within 24 hours for
cohort 3 and 4 by pig age in site A……………………………….…………………………….236
Figure 4.10.c. Box plot of the pen temperature humidity index (THI) index within 24 hours for
cohort 5 and 6 by pig age in site A……………………………….…………………………….236
Figure 4.11.a. Box plot of the pen temperature humidity index (THI) index within 24 hours for
cohort 1 and 2 by pig age in site B……………………………….……………………………..237
xv

Figure 4.11.b. Box plot of the pen temperature humidity index (THI) index within 24 hours for
cohort 3 and 4 by pig age in site B……………………………….……………………………..237
Figure 4.11.c. Box plot of the pen temperature humidity index (THI) index within 24 hours for
cohort 5 and 6 by pig age in site B……………………………….……………………………..238
Figure 4.12.a. Box plot of the pen temperature humidity index (THI) index within 24 hours for
cohort 1 and 2 by pig age in site C……………………………….……………………………..238
Figure 4.12.b. Box plot of the pen temperature humidity index (THI) index within 24 hours for
cohort 3 and 4 by pig age in site C……………………………….……………………………..239
Figure 4.12.c. Box plot of the pen temperature humidity index (THI) index within 24 hours for
cohort 5 and 6 by pig age in site C……………………………….……………………………..239

xvi

ABBREVIATIONS
ACTH

Adrenocorticotropic hormone

AIAO

All in All out

AUC

Area under the curve

BP

base pairs

BPW

Buffered peptone water

CFU

Colony-forming unit

CNS

Central nervous system

Cq

Quantification cycle

DCPAH

Diagnostic Center for Population and Animal Health

ELISA

Enzyme-linked immunosorvent assay

ETEC

Enterotoxigenic E .coli

FSIS

Food Safety and Inspection Service

GN

Gram negative

IQC

Integrated quality control

LCT

Lower critical temperature

LPS

Lipopolysaccharide

MPN

Most probably number

MRSV

Modified semisolid Rappaport Vassiliadis

NAHMS

National Animal Health Monitoring System

OD

Optical density

PCR

Polymerase Chain Reaction
xvii

PRRSV

Porcine Reproductive and Respiratory Syndrome Virus

PRCV

Porcine Respiratory Coronavirus

QMRA

Quantitative microbial risk assessment

q PCR

Quantitative real-time polymerase chain reaction

RH

Relative humidity

ROC

Receiver operating characteristic curve

RV

Rappaport Vassiliadis broth

SOP

Standard operating procedure

S/P

Proportion of absorbance between the sample and a positive control

SPF

Specific pathogen free program

Tdb

Dry bulb temperature

Tdp

Dew point temperature

Twb

Wet bulb temperature

THI

Temperature humidity index

TNZ

Thermo neutral zone

TTB

Tetrathionate broth

UCT

Upper critical temperature

XTL4

Xylose Lysine Tergitol 4

ZAP

Zoonosis Action Plan

xviii

INTRODUCTION

1

INTRODUCTION
Foodborne pathogens cause an estimated 9.4 million foodborne illnesses, 55,961
hospitalizations and 1,351deaths each year in the United States (Scallan et al., 2011). It has been
well documented that Salmonella species are one of the major causes of foodborne diseases in
the US and worldwide (Greig and Ravel, 2009; Anon, 2011; Henao et al., 2011; Scallan et al.,
2011). In the US alone, it is estimated that 1,027 million nontyphoidal Salmonella human
infections result in 19,336 hospitalizations and 378 deaths annually (Scallan et al., 2011) and
costs $ 365 billion in direct medical expenditures annually (Anon, 2011). Salmonella is still one
the most important bacteriological zoonotic hazards transmissible from pork to consumers (Fosse
et al., 2009). A significant number of human cases of salmonellosis (1% to 25%) have been
related to consumption of pork and pork products (Berends et al., 1998; Hald et al., 2006; Ravel
et al., 2009; EFSA, 2010; Guo et al., 2011). Reduction of the Salmonella contamination of pork
and pork products requires interventions at three stages: pre-harvest (farm), harvest (slaughter)
and post-harvest (distribution systems and consumer) (Lo Fo Wong et al., 2002; Boyen et al.,
2008). In the US there are two types of surveillance programs for Salmonella in swine, one at
the slaughterhouse by the Food Safety and Inspection Service (FSIS) (sampling carcasses) and
the other by the National Animal Health Monitoring System (NAHMS) (sampling on farm)
(Bush et al., 2002; USDA-FSIS, 2010). No national Salmonella control program in swine
production has been adopted in contrast to several European countries (Hautekiet et al., 2008;
Abrahantes et al., 2009; Baptista et al., 2010; Snary et al., 2010; Merle et al., 2011). Therefore
the strategies to reduce Salmonella at the farm are dependent on the individual producers’
practices. Identification of effective control measures at the farm level might have better
acceptance by swine producers if those measures have an impact on pig health and production as
2

well as food safety outcomes. In order to put in place on-farm control and intervention measures
it is crucial to understand Salmonella infection dynamics in swine and identify risk factors which
might be a target for interventions at the farm.
The dynamics of Salmonella infection in pigs and farms is complex. In last 20 years, a
large body of literature has been published about cross-sectional studies which investigated
mainly Salmonella prevalence and herd risk factors in swine. A limited number have assessed
the fecal prevalence over time, with longitudinal studies showing high variability in Salmonella
shedding at the farm, cohort and individual animal levels (Funk et al., 2001b; Beloeil et al.,
2003; Kranker et al., 2003; Nollet et al., 2005; Rajic et al., 2005; Farzan et al., 2008; Dorr et al.,
2009; Rostagno et al., 2012). However, few studies have investigated sources of variability of
Salmonella prevalence in swine as well risk factors associated with each level of variability
(farm, cohort, pen, pig and individual sample) (Funk et al., 2001a; Funk et al., 2007; Poljak et al.,
2008). Organizational levels that explain the greatest amount of variation are considered the best
for targeting interventions (Dohoo et al., 2001; Funk et al., 2007). The variability of the
prevalence reported in those studies might be associated with factors related to introduction and
maintenance of salmonellae in the herd environment; that is, transmission between pigs (or
within herd) and persistence of Salmonella in the individual pig (Zheng et al., 2007; Hautekiet et
al., 2008). In order to understand the transmission and persistence in the host and environment,
one critical component to investigate is the shedding patterns and bacterial load in naturally
infected pigs. A limited number of studies have quantified Salmonella concentration in the feces
of naturally infected swine. These were either cross-sectional studies (Fravalo et al., 2003; Fablet
et al., 2006; van Hoek et al., 2012) or estimates of pen-contamination in lairage (O'Connor et al.,
2006; Boughton et al., 2007). Therefore, little is known regarding Salmonella concentration
3

shed by pigs and how it changes over time. Identification of quantification methods that allow
enumeration of Salmonella in a large number of samples in an efficient time-cost and automated
way (Malorny et al., 2008; Elizaquivel et al., 2011; Löfström et al., 2011) are warranted.
Enumeration of bacterial load can be used to identify contamination pressure and to identify
effective control measures to reduce contamination in swine herds (Fravalo et al., 2003). In
addition, data are needed for quantitative microbial risk assessments and for modeling
transmission patterns of Salmonella (Bollaerts et al., 2009; Lanzas et al., 2011).
Seasonal patterns of foodborne diseases have been observed in temperate climates. For
example, human illness caused by Salmonella spp rises in summer and decreases in the winter
(Naumova et al., 2007). Seasonal variation of those foodborne diseases has been related with
oscillations of several environmental factors (Naumova et al., 2007). Among those
environmental factors, high ambient temperature has been consistently associated with human
salmonellosis worldwide (Bentham and Langford, 2001; D'Souza et al., 2004; Fleury et al.,
2006; Naumova et al., 2007). Overall, the association between the mean and highest temperature
several weeks prior the onset of the human cases suggest that the temperature might affect the
Salmonella dynamics at the farm level. Those effects might be to either create an environment
favorable for the proliferation of bacteria in the environment and consequently increase the
bacterial pressure and exposure to the livestock animals, or increase animal susceptibility to new
infections or cause recurrence of existing infections. Unlike the human salmonellosis reports,
there is no agreement regarding seasonal patterns of Salmonella prevalence/shedding in swine.
On one hand some studies reported no seasonality (Benschop et al., 2008; Baptista et al., 2010)
while others reported higher prevalence during different seasons, either by higher seroprevalence
in winter and fall (Carstensen and Christensen, 1998; Christensen and Rudemo, 1998; Hald and
4

Andersen, 2001; Smith et al., 2010) or summer (Hautekiet et al., 2008) and higher fecal
prevalence in winter and spring (Funk et al., 2001a). Season is characterized by environmental
changes of temperature, humidity, precipitation and wind (Dowell, 2001; Naumova, 2006).
Environmental factors such as temperature, rainfall, and sunshine have been associated with
Salmonella prevalence in swine. Finishing pigs exposed to wide variations in daily high
temperature were at greater risk of high Salmonella prevalence (Funk et al., 2001a). In addition,
large differences in long-term averages in the monthly mean temperature, as well as high actual
rainfall and hours of sunshine were associated with higher Salmonella seroprevalence in UK pigs
(Smith et al., 2010). In both studies the environmental parameters were retrieved from the
closest weather station. Therefore, the environmental parameters might not reflect the
environment in the barns. Moreover, herds that had a controlled programmed temperature above
the upper critical values of thermal neutral zone (TNZ) had a higher seroprevalence compared
with herds with controlled programmed temperature within the TNZ (Hautekiet et al., 2008). A
limitation of all of these studies is that they focused on investigation of risk factors at the herd
level and were cross-sectional study designs. Nevertheless, these studies suggest that suboptimal temperature and temperature variability appear to be an important factors associated with
Salmonella infection in swine (Funk et al., 2001a; Funk and Gebreyes, 2004; Hautekiet et al.,
2008; Smith et al., 2010). There is a lack of knowledge of risk factors at the pig-level and timedependent risk factors, namely environmental thermal parameters within the barn and the
association with Salmonella dynamics. Longitudinal studies with repeated sampling on farm,
cohorts, and pigs are needed to investigate time

variant risk factors, such as environmental

temperature (Funk et al., 2001b; Poljak et al., 2008). Interventions that target the thermal
environment may have an effect on reducing the Salmonella shedding in swine and also improve
5

pig well

being and production efficiency. Identification of interventions for control of

Salmonella in swine and simultaneously improve production performance increases the
probability of adoption by swine producers. Moreover, thermal parameters may be used to
identify groups of pigs at high risk for Salmonella shedding, which might require interventions
either in pre-harvest or during harvest to reduce the risk of Salmonella contamination of swine
carcasses.
The studies described in this thesis are components of a longitudinal study conducted on
a multi-site farrow-to-finish production system located in the Midwestern U.S., from June of
2008 to August of 2011. The main goal of the study was to investigate the association between
exposure to sub-optimal thermal parameters and Salmonella shedding in finishing pigs. The
underlying hypothesis of this study is: There is an association between sub

optimal thermal

parameters in the barn and Salmonella shedding in finishing pigs. The specific research
objectives of this thesis are: 1) To describe the shedding pattern of Salmonella in feces of
naturally infected finishing pigs; 2) To compare direct q-PCR detection of Salmonella in swine
feces to the gold standard of microbiological culture; 3) To quantify the fecal concentration of
Salmonella in naturally infected pigs; 4) To evaluate the association between the environmental
thermal parameters in the barn and Salmonella shedding in finishing pigs; 5) To estimate the
proportion of total model variance attributable to cohort, pig and individual sample level effects
when predicting the Salmonella shedding in swine. This thesis is organized in the following four
chapters: 1) Chapter 1, a literature review, which describes background information related to
Salmonella in swine and provides justification for the study; 2) Chapter 2, a longitudinal study of
Salmonella shedding in naturally infected finishing pigs, which describes Salmonella apparent
prevalence in various units of observation (site, cohort, pig age and individual fecal sample) and
6

describes the Salmonella shedding pattern in feces of naturally infected finishing pigs; 3)
Chapter 3, a study regarding the use of direct quantitative real-time PCR for enumeration of
Salmonella in feces of naturally infected pigs; 4) Chapter 4, a multilevel analysis to evaluate the
association between environmental thermal parameters and Salmonella shedding in finishing
pigs. Chapters 2

4 in this thesis were each written and formatted as independent papers

intended for publication in scientific journals, and as such some repetition is inevitable.

7

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8

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Fablet, C., Robinault, C., Jolly, J.P., Collet, M., Chemaly, M., Labbe, A., Madec, F., Fravalo, P.,
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Fleury, M., Charron, D.F., Holt, J.D., Allen, O.B., Maarouf, A.R., 2006. A time series analysis of
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Canadian provinces. Int. J. Biometeorol. 50, 385-391.
Fosse, J., Seegers, H., Magras, C., 2009. Prevalence and risk factors for bacterial food-borne
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Fravalo, P., Hascoet, Y., Fellic, M.L., Queguiner, S., Petton, J., Salvat, G., 2003. Convenient
method for rapid and quantitative assessment of Salmonella enterica contamination: the
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Funk, J., Gebreyes, W.A., 2004. Risk factors associated with Salmonella prevalence on swine
farms. Journal of Swine Health and Production 12, 246-251.
10

Funk, J., Wittum, T.E., LeJeune, J.T., Rajala-Schultz, P.J., Bowman, A., Mack, A., 2007.
Evaluation of stocking density and subtherapeutic chlortetracycline on Salmonella
enterica subsp enterica shedding in growing swine. Vet. Microbiol. 124, 202-208.
Funk, J.A., Davies, P.R., Gebreyes, W., 2001a. Risk factors associated with Salmonella enterica
prevalence in three-site swine production systems in North Carolina, USA. Berl. Munch.
Tierarztl. Wochenschr 114, 335-338.
Funk, J.A., Davies, P.R., Nichols, M.A., 2001b. Longitudinal study of Salmonella enterica in
growing pigs reared in multiple-site swine production systems. Vet. Microbiol. 83, 45-60.
Greig, J.D., Ravel, A., 2009. Analysis of foodborne outbreak data reported internationally for
source attribution. Int. J. Food Microbiol. 130, 77-87.
Guo, C.F., Hoekstra, R.M., Schroeder, C.M., Pires, S.M., Ong, K.L., Hartnett, E., Naugle, A.,
Harman, J., Bennett, P., Cieslak, P., Scallan, E., Rose, B., Holt, K.G., Kissler, B.,
Mbandi, E., Roodsari, R., Angulo, F.J., Cole, D., 2011. Application of bayesian
techniques to model the burden of human salmonellosis attributable to U.S. food
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Hald, T., Andersen, J.S., 2001. Trends and seasonal variations in the occurrence of Salmonella in
pigs, pork and humans in Denmark, 1995-2000. Berl. Munch. Tierarztl. Wochenschr 114,
346-349.
Hald, T., Wingstrand, A., Brondsted, T., Wong, D., 2006. Human health impact of Salmonella
contamination in imported soybean products: a semiquantitative risk assessment.
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Hautekiet, V., Geert, V., Marc, V., Rony, G., 2008. Development of a sanitary risk index for
Salmonella seroprevalence in Belgian pig farms. Prev. Vet. Med. 86, 75-92.
Henao, O.L., Scallan, E., Mahon, B., Hoekstra, R.M., 2011. Methods for monitoring trends in the
incidence of foodborne diseases: foodborne diseases active surveillance network 19962008. Foodborne Pathog. Dis. 7, 1421-1426.
Kranker, S., Alban, L., Boes, J., Dahl, J., 2003. Longitudinal study of Salmonella enterica
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Lo Fo Wong, D.M.A., Hald, T., van der Wolf, P.J., Swanenburg, M., 2002. Epidemiology and
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11

Löfström, C., Schelin, J., Norling, B., Vigre, H., Hoorfar, J., Rådström, P., 2011. Cultureindependent quantification of Salmonella enterica in carcass gauze swabs by flotation
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Naumova, E.N., Jagai, J.S., Matyas, B., DeMaria, A., MacNeill, I.B., Griffiths, J.K., 2007.
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Nollet, N., Houf, K., Dewulf, J., Duchateau, L., De Zutter, L., De Kruif, A., Maes, D., 2005.
Distribution of Salmonella strains in farrow-to-finish pig herds: a longitudinal study. J.
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O'Connor, A.M., Gailey, J., McKean, J.D., Hurd, H.S., 2006. Quantity and distribution of
Salmonella recovered from three swine lairage pens. J. Food Prot. 69, 1717-1719.
Poljak, Z., Dewey, C.E., Friendship, R.M., Martin, S.W., Christensen, J., 2008. Multilevel
analysis of risk factors for Salmonella shedding in Ontario finishing pigs. Epidemiol.
Infect. 136, 1388-1400.
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K., Dewey, C.E., McEwen, S.A., 2005. Longitudinal study of Salmonella species in 90
Alberta swine finishing farms. Vet. Microbiol. 105, 47-56.
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Rostagno, M.H., Hurd, H.S., McKean, J.D., 2012. Variation of bacteriologic and serologic
Salmonella enterica prevalence between cohorts within finishing swine production farms.
Food Res. Int. 45, 867-870.
Scallan, E., Hoekstra, R.M., Angulo, F.J., Tauxe, R.V., Widdowson, M.A., Roy, S.L., Jones,
J.L., Griffin, P.M., 2011. Foodborne illness acquired in the United States major
pathogens. Emerg. Infect. Dis 17, 7-15.
12

Smith, R.P., Clough, H.E., Cook, A.J.C., 2010. Analysis of meat juice ELISA results and
questionnaire data to investigate farm level risk factors for Salmonella infection in UK
pigs. Zoonoses Public Health 57, 39-48.
Snary, E.L., Munday, D.K., Arnold, M.E., Cook, A.J., 2010. Zoonoses action plan Salmonella
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USDA-FSIS, 2010. Serotypes profile of Salmonella isolates from meat and poultry products
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van Hoek, A., de Jonge, R., van Overbeek, W.M., Bouw, E., Pielaat, A., Smid, J.H., Malorny, B.,
Junker, E., Lofstrom, C., Pedersen, K., Aarts, H.J.M., Heres, L., 2012. A quantitative
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Salmonella seropositive slaughter pigs and the presence of herd level risk factors for
introduction and transmission of Salmonella in 34 Danish organic, outdoor (non organic)
and indoor finishing-pig farms. Livest. Sci. 106, 189-199.

13

CHAPTER 1
Literature Review

14

SALMONELLA TAXONOMY
Salmonella was first isolated from pigs by Salmon and Smith, in 1886 and named
Salmonella Choleraesuis because it was thought to be the cause of hog cholera (Fedorka-Cray et
al., 2000). Salmonella is a genus of bacteria belonging to the family Enterobacteriaceae. The
bacteria belonging to this genus are gram-negative straight rods, generally motile, facultative
anaerobes, grow on nutrient agar, ferment glucose, often produce gas and oxidase negative
(Grimont et al., 2000).
The genus Salmonella includes two species: Salmonella enterica and Salmonella
bongori. Salmonella enterica is divided in 6 subspecies: S. enterica supsp. enterica (I); S.
enterica subsp. salamae (II); S. enterica subsp. arizonae (IIa), S. enterica subsp. diarizonae
(IIIb); S. enterica subsp. houtenae (IV) and S. enterica subsp. indica (VI). S. enterica subsp.
enterica affects warm-blooded animals, whereas the other 5 species are found in cold

blooded

animals and in the environment (Brenner et al., 2000; Popoff et al., 2001; CDC, 2008).
There are more than 2,500 serovars of Salmonella enterica identified to date. The serotypes are
defined by the existence of somatic antigens (O, sugar and protein coats on the bacterial surface),
flagellar antigens (H, flagellar proteins), and surface (Vi) antigens (Brenner et al., 2000; Grimont
et al., 2000; Callaway et al., 2008; CDC, 2008). The majority of serovars belong to S.enterica
susp. enterica (59%); among these the most common O antigen groups are A, B, C1, C2, D and
E. These six O antigen groups are responsible for approximately 99% of human and warmblooded animal infections (Brenner et al., 2000). Some serovars can infect a wide range of hosts
while others are host-specific or host-restricted. For instance, S. Choleraesuis, a swine-specific
serovar, is generally associated with severe systemic disease in swine. Other serovars, such as S.
Typhimuirium and S. Enteritidis, can infect a broad range of unrelated hosts, including humans
15

(Fedorka-Cray et al., 2000; Barrow et al., 2010). Among the 2,500 Salmonella enterica
serovars, only a few have been isolated from swine (Brenner et al., 2000; Boyen et al., 2008;
Callaway et al., 2008). On other hand, the most common serovars isolated in swine (Bush et al.,
2002; USDA-APHIS, 2009) are common to those found in human cases. Those serovars are S.
Typhimurium, S. Heidelberg, S. Agona, and S. Infantis (Foley et al., 2008; Anon, 2010).
Salmonellae can be found in mammals, birds, reptiles, insects, rodents and environmental
niches such as water, food, soil and contaminated environments. The natural habitat of
Salmonella is considered the digestive tract of warm and cold-blooded animals. The presence of
Salmonella in the environment results from contamination with fecal material of infected
animals. The bacteria are ubiquitous, can multiply over a wide temperature range (7 C 45 C),
and can persist for months to years in the environment (Murray, 2000; Griffith et al., 2006).

IMPORTANCE OF SALMONELLA IN PUBLIC HEALTH
Foodborne pathogens cause an estimated 9.4 million foodborne illnesses, 55,961
hospitalizations and 1,351deaths each year in the United States (Scallan et al., 2011). Although
the actual costs of foodborne disease in the US are not fully known, in 1996 it was estimated that
costs stemming from medical costs and productivity losses ranged between $6.5 to $34.9 billion
(based on a total population of 250 million) (Buzby and Roberts, 1996 ). Salmonella,
Campylobacter, Listeria, shiga toxin-producing Escherichia coli, Shigella, Clostridium, Vibrio
and Yersinia are enteric pathogens commonly transmitted through food (Greig and Ravel, 2009;
Vugia et al., 2009; Anon, 2011).

16

It has been well documented that Salmonella species are one of the major causes of
foodborne diseases in the US and worldwide (Greig and Ravel, 2009; Anon, 2011; Henao et al.,
2011; Scallan et al., 2011). In the US alone, it is estimated that 1,027 million nontyphoidal
Salmonella human infections result in 19,336 hospitalizations and 378 deaths annually (Scallan
et al., 2011) and cost $ 365 billion in direct medical expenditures annually (Anon, 2011).
Salmonella typically causes mild-to moderate self-limited gastroenteritis, but serious disease
resulting in death can also occur (Trevejo et al., 2003; Voetsch et al., 2004). Hospitalization and
death rates are higher among young children, the elderly, immune compromised patients, males
and certain ethnic groups (Trevejo et al., 2003; Cummings et al., 2010). Despite the Pathogen
Reduction: Hazard Analysis and Critical Control Point (PRA, HACCP) program implemented in
1996 in US slaughterhouses to reduce Salmonella at harvest, the included measures have had
little impact on the human incidence rate (Rose et al., 2002; Davies, 2011). The incidence of
human salmonellosis in the US has not declined over the past 15 years; in fact, cases have
increased about 10% in 2010 when compared with 2006

2008. The incidence rate in 2010 was

not significantly different than the incidence prior to implementation of the HACCP Pathogen
Reduction Act (Anon, 2011).
A wide range of Salmonella serovars have been isolated from human cases (CDC,
2008). According to the FoodNet’s 2010 report, the three most common serovars representing
a majority of the human salmonellosis infections (92%) included: Salmonella enterica
Enteritidis (22%), Newport (14%) and Typhimurium (13%) (Anon, 2011). Those serovars are
common to humans and livestock species (meat and poultry products). Livestock species are
considered the reservoir of many serovars that can infect humans (Clothier et al., 2010; Anon,
2011). However, making the link between livestock species and human illness is not simple.
17

The attribution of foodborne human illness to specific sources is complex and traditionally relies
on microbiological approaches, epidemiological approaches, intervention studies and/or expert
opinion (Pires et al., 2009). The link between the cases in humans and infections in livestock or
to specific food sources is not straightforward (Batz et al., 2005; Pires et al., 2009). A majority
of the studies attribute human salmonellosis to consumption of contaminated food such as meat,
eggs and fresh produce. Several studies have been done to estimate food or commodity
attribution for human salmonellosis in North America and Europe (Greig and Ravel, 2009; Ravel
et al., 2009; Guo et al., 2011). The most common sources of human salmonellosis outbreaks in
Europe were eggs (32%) and meat and poultry meat products (15%) (Pires et al., 2010). Fortytwo percent of cases have no known source (Pires et al., 2010). In the US, an estimate of relative
contribution to domestically acquired sporadic human Salmonella infections was highest for
chicken (48%) and ground beef (28%), followed by turkey (17%), egg products (6%), intact beef
(1%) and pork (less than 1%) (Guo et al., 2011). In contrast, produce had the highest
contribution (29%) to human salmonellosis in Canadian outbreaks, follow by poultry (15%), and
meat other than poultry, pork and beef (15%) (Ravel et al., 2009).
Salmonella is still one of the most important bacteriological zoonotic hazards
transmissible from pork to consumers (Fosse et al., 2008, 2009; Fosse et al., 2011). Despite pork
being estimated to have a very low attribution rate for foodborne cases compared to other food
vehicles (in the US), statistical models have predicted that every year approximately 100,000
human cases of salmonellosis associated with the consumption of pork resulting economic costs
of about $ 80 million in the US (Miller et al., 2005). Contaminated pork is still considered an
important hazard to public health (Boyen et al., 2008). In the US, Salmonella has been isolated
in pork and pork products at the slaughterhouse (Carlson and Blaha, 2001; Bahnson et al., 2006a;
18

Gebreyes et al., 2006) and in retail pork (Duffy et al., 2001; Mollenkopf et al., 2011). Reduction
of the Salmonella contamination of pork and pork products requires interventions at three levels:
pre

harvest (farm), harvest (slaughter) and post

harvest (distribution systems and consumer)

(Lo Fo Wong et al., 2002; Boyen et al., 2008). A stochastic cost-effectiveness study for
controlling Salmonella in the pork production chain showed that interventions in the finishing
and slaughtering stages are most cost-effective in reducing the prevalence of contaminated
carcasses (van der Gaag et al., 2004).

PATHOGENESIS OF SALMONELLA IN SWINE
Clinical and subclinical syndromes
Salmonella infection in swine is mainly subclinical as pigs can be asymptomatic carriers.
Swine can be infected by host-adapted serovars such as S. Choleraesuis var. Kunzendorf, and
broad host-range serovars such as S. Typhimurium. Two clinical forms can be observed in
swine: septicemia caused by S. Choleraesuis, and enterocolitis caused by S. Typhimurium (Reed
et al., 1986; Fedorka-Cray et al., 2000; Barrow et al., 2010). Although other serovars can cause
clinical disease, the top four serovars recovered from clinical cases submitted to the Iowa State
University Veterinary Diagnostic Laboratory (ISU VDL) in 2003 and 2008 were: Typhimurium
var. 5-, Choleraesuis var. Kunzendorf , Derby, Typhimurium and Heidelberg (Clothier et al.,
2010). An observed decline of isolation of Choleraesuis var. Kunzendorf, and increased
isolation of Typhimurium in US veterinary laboratories has been reported (Foley et al., 2008;
Clothier et al., 2010).

19

Pigs can be infected with Salmonella at any age, but clinical cases due to S. Typhimurium
are more common at 6 to 12 weeks old, while S. Choleraesuis var. Kunzendorf, can cause
clinical disease at a wider range of ages (Fedorka-Cray et al., 2000). Clinical signs of the
septicemic syndrome include: fever, depression, respiratory signs, cyanotic extremities and
death. In the case of septicemic outbreaks the mortality is high and morbidity is in generally
low. Salmonella enterocolitis is characterized by diarrhea, lethargy and fever. In this clinical
presentation, morbidity can be high and mortality is low (Reed et al., 1986; Fedorka-Cray et al.,
2000).

Sources of infection
In general, the major sources of infection are other infected pigs, since they are the main
reservoir and environments are focally contaminated by pigs (Berends et al., 1996; Murray,
2000; Funk and Gebreyes, 2004; Griffith et al., 2006). The source of infection of host-adapted
serovars (Choleraesuis var. Kunzendorf) is mostly due to infected pigs and environments
contaminated by pigs. For other non-specific serovars (S. Derby, Typhimurium, Agona, etc), a
diverse range of environments, vectors and fomites have been suggested as potential sources of
infection (Berends et al., 1996; Murray, 2000; Funk and Gebreyes, 2004; Griffith et al., 2006).
Vectors such as flies, rodents, cats, birds, and wild animals are potential vehicles and sources of
Salmonella (Letellier et al., 1999; Barber et al., 2002; Funk and Gebreyes, 2004). In addition,
Salmonella has been isolated from diverse fomites in barns such as dust, pens, floors, boots,
feeders and waters (Berends et al., 1996; Letellier et al., 1999; Barber et al., 2002; Rajic et al.,
2005). A contaminated environment poses a risk for long periods, because the organism can
20

remain viable and infective in several environments for more than 13 months (Murray, 2000;
Gray and Fedorka-Cray, 2001; Griffith et al., 2006). Feed has also been identified as a source
and risk factor (Berends et al., 1996; Funk and Gebreyes, 2004; Griffith et al., 2006). Salmonella
has been isolated from feed at the farm, feed mill and from feed transportation vehicles in several
studies, but not always was the same serovar identified in the feed and in the pigs consuming the
feed (Berends et al., 1996; Funk and Gebreyes, 2004).

Transmission, dose and serovars
Transmission of Salmonella in swine can occur via direct contact (pig to pig,
dam to offspring), or through indirect contact with vectors, fomites (Letellier et al., 1999;
Barber et al., 2002; Rajic et al., 2005) or contaminated environments (contaminated
environment to pig) (Fedorka-Cray et al., 1994; Loynachan and Harris, 2005; Griffith et al.,
2006; Osterberg et al., 2010). The transmission occurs within a short period of time after pigs
have been exposed to a contaminated environment (Fedorka-Cray et al., 1994; Hurd et al.,
2001a). Pigs can become infected 30 minutes after exposure to a contaminated slurry with a
3

minimum concentration of 10 CFU (colony forming units) (Hurd et al., 2001a). The contact
3

with a contaminated environment of least 10 salmonellae per gram of feces leads to an acute
infection of both alimentary and non-alimentary tissues (Hurd et al., 2001a; Loynachan and
Harris, 2005).
The route of transmission of Salmonella between pigs is mainly feco

oral, but other

routes such as nose to nose or airborne can occur (Fedorka-Cray et al., 1994; Fedorka-Cray et
al., 1995; Proux et al., 2001; Oliveira et al., 2006; Oliveira et al., 2007). Pigs that were orally
21

challenged with 10

10

CFU of S. Typhimurium show clinical signs after 48 h with shedding of

Salmonellae that can infect other pigs either by commingling or contact with contaminated fecal
material (Fedorka-Cray et al., 1994). Alternative routes of transmission have been tested
experimentally. Salmonella Typhimurium was isolated in several gut and gut related tissues after
intranasal challenge in esophagotomized pigs (Fedorka-Cray et al., 1995). The upper respiratory
tract and lungs can be important sites for transmission and invasion of Salmonella (Fedorka-Cray
et al., 1995; Proux et al., 2001; Oliveira et al., 2006; Oliveira et al., 2007). Airborne
transmission is possible at short distances but may depend on the serovar (Oliveira et al., 2006;
Oliveira et al., 2007).
The infectious dose of Salmonella is variable and dependent on transmission route and
serovar. The majority of the experimental studies have used high doses of salmonellae, ranging
6

from 10 to 10

10

CFU S. Typhimurium (Wood et al., 1989; Wood and Rose, 1992; Fedorka-

Cray et al., 1994; Fedorka-Cray et al., 1995; Osterberg and Wallgren, 2008; Scherer et al., 2008)
7

8

and 10 to 10 CFU S. Choleraesuis (Gray et al., 1995; Gray et al., 1996a; Anderson et al.,
3

2000). There is a dose dependency for Salmonella to be infectious. Doses greater than 10 CFU
of S. Choleraesuis or S. Typhimurium are necessary to be able to induce acute Salmonella
infection and fecal detection (Gray et al., 1996b; Loynachan and Harris, 2005; Osterberg and
Wallgren, 2008). In addition, the challenge dose affects the length of fecal shedding and the
persistence in tissues (Gray et al., 1996b; Osterberg and Wallgren, 2008; Osterberg et al., 2009).
Pigs infected with higher doses shed for longer periods. Moreover, higher doses are required in
order to lead to clinical disease and to create long-term carriers (Gray et al., 1996b; Osterberg
22

and Wallgren, 2008; Osterberg et al., 2009). The transmission of Salmonella might be serovardependent (van Winsen et al., 2001; Osterberg et al., 2010). For instance, the transmission
between seeders (infected pigs) and sentinels (naïve pigs) of S. Goldcoast and S. Panama occur at
a lower rate when compared with S. Typhimurium and S. Livingstone when inoculated with
similar challenge (van Winsen et al., 2001).
Salmonella can be isolated in alimentary and non-alimentary tissues. The dissemination
of Salmonella in pigs occurs rapidly after per os or intranasal exposure. Several Salmonella
serovars were isolated in tissues 3 hours after intranasal inoculation (Loynachan et al., 2004;
Loynachan and Harris, 2005) and in 30 minutes after exposure to contaminated slurry (Hurd et
al., 2001a). In terms of alimentary tissues the most common tissues from which Salmonella are
isolated post challenge are: tonsils, illeum, jejunum and cecum (Wood et al., 1989; Wood and
Rose, 1992; Fedorka-Cray et al., 1994; Gray et al., 1995; Loynachan et al., 2004; Loynachan and
Harris, 2005). The ileocecal/ileocolic and manibular lymph nodes and lungs are the most
common non

alimentary tissues (Wood et al., 1989; Wood and Rose, 1992; Fedorka-Cray et

al., 1994; Gray et al., 1995; Loynachan et al., 2004; Loynachan and Harris, 2005). In the initial
stages of infection, tonsils are an important site (primary site) for colonization. Salmonella can
persist in tonsils for a long period after the exposure (Wood et al., 1989; Wood and Rose, 1992;
Fedorka-Cray et al., 1994; Fedorka-Cray et al., 1995). Isolation of Salmonella in cecal contents
or rectal swabs is frequent in all stages post- infection. Experimentally infected pigs shed high
levels of Salmonella in feces within a short period post-infection, declining with time. The
duration of shedding has been reported to be as short as seven days and as long as 28 weeks
(Wood and Rose, 1992; Scherer et al., 2008).

23

Carriers and intermittent shedders
Infection with S. Choleraesuis (Gray et al., 1995) or S. Typhimurium can result in
long term subclinical carriers (12 weeks to 28 weeks post infection) (Wood et al., 1989; Wood
and Rose, 1992; Fedorka-Cray et al., 1994; Scherer et al., 2008). The carrier status can persist
up to 28 weeks in pigs orally challenged with S. Typhimurium (Wood et al., 1989). Persistently
infected pigs have relatively low concentrations of Salmonella either in intestinal contents or
lymph nodes (Wood and Rose, 1992). Weaned pigs orally challenged with S. Typhimurium can
6

shed as high as 10 CFU/g feces in the first 7 days post infection. After 60 days post infection
the shedding rate decreased to levels less than 10 CFU /g and remaines intermittent during a five
month study period (Scherer et al., 2008). In addition to a low concentration of bacteria shed,
pigs also can become intermittent shedders with time (Osterberg and Wallgren, 2008; Scherer et
al., 2008). Intermittent shedding has been observed in experimental infections with several
different serovars, such as: S. Choleraesuis; S. Typhimurium; S. Derby, S. Cubana; S. Yoruba
(Nielsen et al., 1995; Gray et al., 1996b; Osterberg and Wallgren, 2008; Osterberg et al., 2009),
as well in observational studies (Beloeil et al., 2003; Kranker et al., 2003; Nollet et al., 2005a).
Intermittent shedders are important not only at the farm as a source of transmission to pen mates
(Kranker et al., 2003), during transportation to the slaughterhouse a reactivation of shedding
when pigs are exposed to the stress of transportation and lairage can occur (Berends et al., 1996;
Larsen et al., 2003; Nollet et al., 2005a).

24

Seroconversion
The host response to Salmonella involves both humoral (Gray et al., 1996b; Holt, 2000;
Osterberg and Wallgren, 2008; Scherer et al., 2008; Osterberg et al., 2009) and cellular immune
responses (Lumsden and Wilkie, 1992; Stabel et al., 1993; Gray et al., 1996b). Two types of
humoral immunity are observed: mucosal and serological. Mucosal immunity includes secretion
of IgA and the humoral production of IgM and IgG (Holt, 2000). The serological (antibody)
response to Salmonella varies by the challenge dose, serovar, and time post infection (Nielsen et
al., 1995; Gray et al., 1996b; Osterberg and Wallgren, 2008; Scherer et al., 2008; Osterberg et
al., 2009). In experimental studies, the onset of the serological response and peak seroprevalence
occur approximately 7 to 14 days and 30 to 45 days post infection, respectively (Nielsen et al.,
1995; Gray et al., 1996b; Osterberg and Wallgren, 2008; Scherer et al., 2008; Osterberg et al.,
2009). The IgM antibody response to the lipopolysaccharide (LPS) antigen of S. Choleraesuis
surges in early post infection, and disappears or remains at low titers after 7 weeks (Gray et al.,
1996b). Conversely, serum IgG antibody to LPS antigen of S. Choleraesuis was detected later
and remained at higher titers for longer periods up to 15 weeks post-infection (Gray et al.,
1996b). Seroconversion is observed in a majority of the pigs experimentally infected with S.
Derby and S. Typhimurium within 14 45 days post infection (Osterberg and Wallgren, 2008;
Scherer et al., 2008; Osterberg et al., 2009). However, lower titers or no detectable
3

6

seroconversion was observed when pigs were exposed to lower doses of 10 to 10 (Osterberg
and Wallgren, 2008; Osterberg et al., 2009). Anti-Salmonella Typhimurium IgG titers remained
high for periods lasting longer than four months post infection (Scherer et al., 2008). The
presence of serum antibodies can be representative of exposure and not necessarily current
25

infection or active shedding. Pigs maintained high antibody titers even after being culture
negative for Salmonella in fecal samples (Scherer et al., 2008). Pigs challenged with certain
serovars do not seroconvert or also show low titers for a short period of time. Feed related
serovars showed less capacity for infection or invasion, and some do not induce seroconversion
(Osterberg and Wallgren, 2008; Osterberg et al., 2009). For example, there was no
seroconversion when pigs were challenged with the feed related serovar S. Cubana (Osterberg
et al., 2009). In naturally infected pigs, longer time periods between the peak prevalence in
culture, the onset of serological response, and peak seroprevalence was observed (Kranker et al.,
2003; Merialdi et al., 2008). The serological response was observed 30 days later than the peak
of Salmonella shedding (around 90 days of age) (Kranker et al., 2003); that is the peak of
seroprevalence was observed between 120 to 270 days of age (Kranker et al., 2003; Merialdi et
al., 2008). The observed difference between experimental and observational studies under
natural conditions can be explained as pigs can be infected at different points in time and also
can be re infected (Kranker et al., 2003).

DIAGNOSIS OF SALMONELLA IN SWINE
The diagnostic tests available for pre harvest Salmonella detection and prevalence
estimation are based on: 1) culture of Salmonella in feces (Funk et al., 2001b; Wilkins et al.,
2010b), 2) identification of Salmonella DNA (e.g. Polymerase Chain Reactions (PCR)
techniques) (Wilkins et al., 2010b) and 3) detection of anti Salmonella antibodies by serology
(Alban et al., 2002; Baptista et al., 2009). At harvest, culture of carcass swabs (Gebreyes et al.,
2006; Baptista et al., 2010b), lymph nodes (Carlson and Blaha, 2001; Hurd et al., 2004) and fecal
26

contents have been used (Hurd et al., 2004). The detection of antibodies in meat juice from the
diaphragm has been used to assess Salmonella antibody concentration (O'Connor et al., 2006b;
McKean and O'Connor, 2009; Baptista et al., 2010b).

Serology
Mix-ELISA assays targeting anti-LPS antibodies have been developed to detect
antibodies to Salmonella serogroups O: 1,4,5,6,7,12 (Nielsen et al., 1995; Nielsen et al., 1998)
and the serogroups O: 1,3,5,6,7,8,9,10,12 (Proux et al., 2000) in serum and muscle. Surveillance
programs in swine based on serological analysis have been implemented in Denmark (Alban et
al., 2002; Baptista et al., 2009; Baptista et al., 2010b), Germany (Merle et al., 2011), Belgium
(Hautekiet et al., 2008) and the UK (Abrahantes et al., 2009; Snary et al., 2010). These serologic
tests are applied either in serum from blood or meat juice. The diagnosis of Salmonella using the
mix-Elisa assays are based on certain optical density (OD) cut-offs (e.g. 20%) (Denmark) (Alban
et al., 2002; Baptista et al., 2009; Baptista et al., 2010b) or based on S/P ratio (a measure of
relative proportion of absorbance between the sample and a positive control) (UK) (Abrahantes
et al., 2009; Snary et al., 2010). Serological tests rely on the presence of antibodies and represent
historical exposure to the bacterium. Serological testing of herds has been used to classify the
herd at different risk levels in order to put in place reduction measures at both farm and slaughter
(Alban et al., 2002; Baptista et al., 2009; Baptista et al., 2010b). Although a good correlation
between the serological and bacteriological classification of herd is observed (Lo Fo Wong et al.,
2004b; Rajic et al., 2007a), there is a poor correlation with individual Salmonella fecal
prevalence (Funk et al., 2005; Rajic et al., 2007a).
27

General Salmonella culture
In the literature there are numerous microbiological methods for isolation of Salmonella
(Waltman, 2000). The detection of Salmonella organisms by culture of swine specimens
depends on specimen type, microbiological culture method, sample aggregation (individual pig
or pool pen) and serovar. Different protocols have been developed to isolate Salmonella from:
carcass swabs (Sorensen et al., 2007), cecal contents (Skovgaard et al., 1985; Harvey et al.,
2001; Champagne et al., 2005), feces (Davies et al., 2000; Funk et al., 2000; Hoorfar and
Mortensen, 2000; Rostagno et al., 2005a; Love and Rostagno, 2008), rectal swabs (Haddock,
1970; Nietfeld et al., 1998), lymph nodes (Harvey et al., 2001) and environmental drag swabs
(Zewde et al., 2009b). Advantages to the use of microbiological culture in comparison to other
diagnostic tests are: 1) to identify viable bacteria, not DNA; 2) to quantify the bacteria load; 3) to
facilitate further testing such as serotyping, genotyping, phage typing and susceptibility testing
(Funk, 2003; Farzan et al., 2007).

Salmonella fecal culture
Numerous studies have compared microbiological methods for the isolation of
Salmonella in fecal material of naturally infected pigs (Skovgaard et al., 1985; Bager and
Petersen, 1991; Hoorfar and Baggesen, 1998; Nietfeld et al., 1998; O'Carroll et al., 1999; Davies
et al., 2000; Funk et al., 2000; Hoorfar and Mortensen, 2000; Harvey et al., 2001; Erdman and
Harris, 2003; Jensen et al., 2003; Osumi et al., 2003; Pangloli et al., 2003; Korsak et al., 2004;
Champagne et al., 2005; Rostagno et al., 2005a; Love and Rostagno, 2008). In general, the
culture methods differ on: 1) type of pre enrichment; 2) type of selective enrichment; 3)
28

enrichment incubation temperature; 4) enrichment incubation time; and 5) plating media. The
pre enrichment step permits the resuscitation of damaged or injured Salmonella organisms
(Hoorfar and Baggesen, 1998; Waltman, 2000; Jensen et al., 2003). The pre enrichment
medium can be non selective or selective. Several non-selective pre enrichment broths have
been used, with buffered peptone water (BPW) being one of the most frequently used as the first
step of the enrichment process (Hoorfar and Baggesen, 1998; Davies et al., 2000; Funk et al.,
2000; Hoorfar and Mortensen, 2000; Waltman, 2000; Erdman and Harris, 2003; Jensen et al.,
2003; Korsak et al., 2004; Love and Rostagno, 2008). Enrichment media are necessary not only
to revitalize damaged salmonellae but also to increase target bacteria numbers. Fecal samples
have a wide variety of background bacteria that can inhibit Salmonella multiplication (Love and
Rostagno, 2008). In addition, subclinical carrier pigs shed low concentrations of Salmonella
(Fravalo et al., 2003; Fablet et al., 2006). Thus if the detection is the objective, selective
enrichment media are required steps in microbiological fecal culture in order to selectively
inhibit other bacteria while allowing Salmonella to multiply to levels to be detected in plating
media. The three most common selective enrichments used in swine fecal culture protocols are:
tetrathionate broth (TTB), Rappaport Vassiliadis (RV) broth, and Gram negative (GN) Hajna,
either in combination with a pre enrichment broth or double selective enrichment (Bager and
Petersen, 1991; Davies et al., 2000; Funk et al., 2000; Hoorfar and Mortensen, 2000; Harvey et
al., 2001; Erdman and Harris, 2003; Rostagno et al., 2005a; Love and Rostagno, 2008). The
medium Xylose Lysine Tergitol 4 (XLT4) (Davies et al., 2000; Funk et al., 2000; Korsak et
al., 2004; Rostagno et al., 2005a; Love and Rostagno, 2008) and modified
Rappaport Vassiliadis (MRSV) (Hoorfar and Baggesen, 1998; Hoorfar and Mortensen, 2000;
29

Jensen et al., 2003; Champagne et al., 2005) are the most frequent agars used to isolate
Salmonella colonies following the growth in enrichments broths.
The temperature of pre enrichment, enrichment, and plating is generally either 37 C or
42 C. The time for incubation varies between 24 hours or 48 hours depending on protocol
(Davies et al., 2000; Funk et al., 2000; Rostagno et al., 2005a; Love and Rostagno, 2008). A
consequence of the multiple enrichment step protocols for isolation of salmonellae is that it is
both labor and cost intensive. Despite being an imperfect diagnostic test, fecal culture is
considered the ‘gold standard’ for Salmonella diagnosis. The sensitivity of a diagnostic test is
affected by analytical and diagnostic sensitivity (Saah and Hoover, 1997; Hurd et al., 2004).
The diagnostic sensitivity is also affected by biological factors related to Salmonella infection
(serovar, stage of infection), prevalence, targeted population, sampling strategy, and time of
sampling (Greiner and Gardner, 2000; Hurd et al., 2004). Other factors that can affect culture
sensitivity include: culture methods (Davies et al., 2000; Funk et al., 2000; Rostagno et al.,
2005a; Love and Rostagno, 2008), fecal sample weight (Funk et al., 2000; Champagne et al.,
2005); sample type (individual fecal versus pool) (Haddock, 1970; Davies et al., 2000;
Rostagno et al., 2005a; Love and Rostagno, 2008; Arnold and Cook, 2009) and serovar (Osumi
et al., 2003; Rostagno et al., 2005a).
Estimates of the relative sensitivity of fecal culture range from 6.5% to 95%, depending
on culture method and parallel estimation of the sensitivity (Davies et al., 2000; Funk et al.,
2000; Funk, 2003; Hurd et al., 2004; Rostagno et al., 2005a; Love and Rostagno, 2008). In
general, independent of the fecal culture method the specificity is considered to be 100% (Funk,
2003; Champagne et al., 2005; Rostagno et al., 2005a). Conversely, a low specificity (10%)
when swine fecal samples were enriched in RV and then plated on XLT4 was reported (Mejia et
30

al., 2005). These findings could be due to the fact that the enrichment was done in one unique
selective broth which is diffferent than the methods used by the other authors.

Fecal culture protocols and test performance
Two culture methods for Salmonella that are routinely performed in epidemiological
studies were compared by Davies et al. (2000). Method 1 used a non selective pre enrichment
(BPW, buffered peptone water), followed by RV enrichment. Method 2 used selective
enrichment in TTB or GN Hajna broth followed by RV enrichment. The total proportions of
positive samples were identical in both methods. However, when considering only samples with
identical weight the selective enrichment broths TTB (74%) and GN Hajna (48%) had a higher
proportion of positive samples compared with BPW (23%). The relative sensitivity of the tests
evaluated in this study ranged from 55 to 74 % (Davies et al., 2000). The lack of a difference
between the non selective and selective methods could be due to weight differences between
fecal samples that directly affects the sensitivity of culture. Funk et al. (2000) compared rectal
swabs, 1 g, 10 g and 25 g fecal samples using the same culture method 1 (pre-enrichment in
BPW, followed by enrichment in RV and XLT4 media). Relative sensitivity was significantly
higher in 25g samples (78.3%) when compared with 10 g (52.2%), 1g (21.2%) and rectal swabs
(8.7%) (Funk et al., 2000). Identical findings were reported by Champagne et al. (2005) with
higher detection of Salmonella in 10 g samples than 1 g with a MSRV protocol (Champagne et
al., 2005). On the other hand, the effect of sample weight on sensitivity is greater when the
number of clusters of organisms is low. The sensitivity increases as the number of the clusters
increases, in other words the sensitivity increases with homogeneity of the distribution of the

31

organisms in a sample (Cannon and Nicholls, 2002). Funk et al. (2000) suggested that a non
homogenous distribution of Salmonella in swine feces could explain why an isolation of bacteria
in 1 g samples but not in 10 g or 25 g samples (Funk et al., 2000). Although differences in the
sensitivity of stomached and non stomached feces was not statistically significant (Funk et al.,
2000), stomaching of enrichment broths is a common practice of many culture protocols
(O'Connor et al., 2006a; Poljak et al., 2008).
Recently, Love and Rostagno (2008) compared five culture protocols for isolation of
Salmonella enterica from fecal samples of naturally infected swine, representing the most
common methods reported in epidemiologic studies of Salmonella in swine. The five culture
methods had different combinations of non selective enrichment broth (BPW); selective
enrichment broth (GN, RV, TTB) and selective /differential agar plates (XLT4, MSRV);
o

o

incubation temperature (37 C or 42 C) and time (24h, 48h). None of the methods identified all
positive samples, when compared with the‘standard test’ as a sample being dectected posive by
at least one of the methods. Based on highest relative sensitivity (91.3%), the recommended
culture method was inoculation of 10 g of feces into 100 ml of TTB with incubation at 37°C for
48 h, a subculture of 10 ml of the TTB into 100 ml RV broth with incubation at 37°C for 24 h,
and then inoculation on XLT4 agar plates with 10 l of RV broth and incubated 24 h at 37°C
(Love and Rostagno, 2008). Nevertheless, when the objective of the study is to determine the
diversity and serovar distribution in a population, a parallel culture methodology should be
considered, since culture methods have differential ability to isolate Salmonella serovars (Bager
and Petersen, 1991; Osumi et al., 2003; Rostagno et al., 2005a). Even at the sample level there
32

may be challenges in identifying all serovars present if only one culture method is used. This
may of particular importance when pooled fecal sampling is used. Because pooled samples are a
combination of 2 or more individual samples it may be more likely that more than one serovar is
present (Rostagno et al., 2005a). However, even individual pigs can shed multiple serovars at
once (O'Carroll et al., 1999; Funk et al., 2000, 2001b). Although higher serovar diversity is
expected in pooled samples than in individual samples, use of one culture method might miss the
identification of certain serotypes in pigs infected with multiple serovars. In addition to multiple
culture methods, it has been suggested to select multiple colonies per plate in order to increase
the probability of detecting more than one serovar within the same sample (Funk, 2003;
Rostagno et al., 2005a).
The sensitivity of fecal culture can be increased by delayed selective enrichment, which
consists of keeping samples in enrichment broth (RV) at room temperature for several days (4 8
days), followed by inoculation in RV (Nietfeld et al., 1998; O'Carroll et al., 1999; Davies et al.,
2000). This additional step can increase the sensitivity by up to 25% (Davies et al., 2000). In
epidemiological studies the culture of fecal samples is often delayed for several hours or days
after collection. The storage of samples for 6 days at 4 C did not affect the sensitivity of
culture methods of swine fecal samples; however the detection of Salmonella decreased when
samples were frozen at 15 C for 14 days (O'Carroll et al., 1999).
In summary, the sensitivity of culture methods can be improved by using multiple
specific enrichment broths (Davies et al., 2000; Rostagno et al., 2005a; Love and Rostagno,
2008) , increasing weight of the sample (Funk et al., 2000), and using delayed secondary
enrichment (Davies et al., 2000).
33

Consequences of imperfect sensitivity of fecal culture
The sensitivity of a diagnostic test is not only affected by the test itself and sampling
characteristics but also by the biological characteristics of the disease or infection/colonization
within host. Pigs can be asymptomatic carriers of Salmonella. An intermittent pattern of
Salmonella shedding has been reported in the literature, in both experimentally infected (Nielsen
et al., 1995; Gray et al., 1996b; Osterberg and Wallgren, 2008; Scherer et al., 2008; Osterberg et
al., 2009) and naturally infected pigs (Funk et al., 2001b; Kranker et al., 2003). Pig status may
change over time, depending upon the time of sampling (Funk et al., 2001b; Funk, 2003).
Obtaining multiple fecal samples from individuals can increase the sensitivity of the test (Funk,
2003; Thurmond and Johnson, 2004) for period prevalence estimates.
In summary, some of the strategies to increase the sensitivity of the culture methods
based on sample and culture methods are: sample weight (Funk et al., 2000), parallel culture,
double enrichment (Davies et al., 2000; Funk, 2003; Rostagno et al., 2005a; Love and Rostagno,
2008), delayed selective methods (O'Carroll et al., 1999; Davies et al., 2000), homogeneity of the
sample (Cannon and Nicholls, 2002) and culture of multiple samples per pig (Funk et al., 2001b;
Funk, 2003; Rostagno et al., 2005a). In a meta-analysis done by Sanchez et al., (2007), the
diagnostic procedure was among the top 3 predictors that most affected the estimation of
Salmonella prevalence either at the farm level or pig level (Sanchez et al., 2007).

Pooled versus individual fecal samples
Microbiological analysis of pooled fecal samples has been widely used in epidemiologic
studies of Salmonella in swine. Pooled fecal samples at the farm have been used to classify herd
34

status, estimate within herd prevalence (Stege et al., 2000a; Stege et al., 2000b; Lo Fo Wong et
al., 2003; Rajic et al., 2005; Farzan et al., 2006; García-Feliz et al., 2007; Farzan et al., 2008a;
Farzan et al., 2008b; Wilkins et al., 2010a) and investigate on-farm risk factors for Salmonella
infection in swine (van der Wolf et al., 1999; Mejia et al., 2006; Rajic et al., 2007b; Poljak et al.,
2008; Wilkins et al., 2010a). Compared to individual sampling, pooling of individual fecal
samples to assess herd or pen prevalence and risk factors offers a cost effective and practical
methodology (Funk, 2003). Two strategies have been used for pooling fecal samples. These
include pooling individual fecal samples of five or more pigs (Funk et al., 2000) or about five
grams of composite fecal material collected in five or more different locations on the pen floor
(Rajic et al., 2005; Farzan et al., 2008a; Wilkins et al., 2010a).
Pooled fecal sampling offers several advantages compared to individual sampling: 1)
there is no need for restraining pigs, thus the stress of handling is minimized (Arnold et al.,
2005; Arnold and Cook, 2009); 2) the required personnel at the farm is reduced; 3) the number
of individuals that can be represented at the same time increases (Christensen and Gardner,
2000; Arnold et al., 2005; Arnold and Cook, 2009); 4) fewer samples are submitted to the
laboratory, reducing costs and burden (Christensen and Gardner, 2000; Arnold and Cook, 2009)
; 5) the diversity of serovars detected within a farm increases (Rostagno et al., 2005a); and 6) the
probability of detecting an intermittent shedder in a pen increases (Arnold et al., 2005; Arnold et
al., 2009).
These benefits are evident in studies comparing pooled of composite and individual fecal
sampling. When pooled fecal samples were compared with individual samples, the proportion
of pooled fecal samples that were positive was higher than the proportion of individual samples
(Farzan et al., 2008a; Arnold and Cook, 2009; Wilkins et al., 2010a). The pool sensitivity
35

increases with the number of positive samples in the pool; pools of 5, 10 and 20 grams are more
sensitive than individual sampling, and pools of 20 have the highest sensitivity for farm or
pen level detection (Arnold et al., 2005). Moreover, the increase in sensitivity with pooled
sampling is greater when the prevalence is low (Christensen and Gardner, 2000).
In general the pooled sample sensitivity and specificity can be affected by several factors
such as dilution, concentration of the analyte (bacteria) and sampling probabilities that each can
consequently affect the herd sensitivity (Christensen and Gardner, 2000). The sensitivity of
pooled fecal sampling specifically regarding Salmonella can be affected by the concentration of
Salmonella in pig feces, sample weight of individual samples, dilution effect if the prevalence is
low and clustering of pigs within pens (Arnold et al., 2005; Arnold and Cook, 2009). The
sensitivity of pooled sampling is greatly reduced due to dilution effect as the proportion of
negative individual samples in each pool increases (Enoe et al., 2003; Arnold et al., 2005;
Arnold and Cook, 2009). The effect of mixing negative samples with positive samples might
also be greater in the presence of clustering of the organisms and small number of clusters (i.e.
lack of homogeneity) (Cannon and Nicholls, 2002). In addition, a greater proportion of
competing micro organisms and inhibitory substances in the negative material relative to the
positive material in pools can further decrease the pool sensitivity (Baggesen et al., 2007). In
contrast, Funk et al., (2000) showed no difference between stomached and non stomached
samples, which theorically should increase homogeneity, for detection of Salmonella in
individual fecal samples (Funk et al., 2000); however, this may not be consistent with results for
pooled samples. On the other hand, there is a decreased sensitivity of pooled fecal samples
when clustering within pens is taken into account (Arnold and Cook, 2009). This should be
taken into account in epidemiologic studies with pen sampling of the herd occurs, since pooled
36

samples can be either collected representing a single pen, or a composite of samples from
multiple pens.
Overall, pooled fecal sampling offers a good alternative to determine herd or pen
prevalence, but in some situations it would not be indicated. If the goal of the study is to
investigate the individual dynamics of Salmonella infection and risk factors at the pig level,
individual sampling is preferred to the pool sampling. In addition, pooled fecal samples might
not be representative of individual pigs. Serovars and phage types at the pen and farm level
were reported to be different when individual pig samples were compared with pooled pen
samples in a Canadian study (Farzan et al., 2008a). In addition, pooled samples obtained from
pen floors may not represent active shedders, as there could be residual contamination in the pen
environment. In terms of feasibility, weight /or volume of the pooled samples might also be a
constraint for laboratory processing (Christensen and Gardner, 2000) .

Quantification of Salmonella
Quantitative methods for enumeration of Salmonella in pigs and pork products are
required for quantitative microbial risk assessments (QMRA), necessary to investigate source
attribution and traceability of foodborne pathogens responsible for human illness (Bollaerts et
al., 2009; Boone et al., 2009; Smid et al., 2011). The quantitative data used in such models are
based mainly on expert opinion due a lack of empirical data (Bollaerts et al., 2009; Boone et al.,
2009; Smid et al., 2011). The majority of QMRA models focus on measures of frequency
(prevalence at the farm, lairage and microbial carcass contamination) (Miller et al., 2005; Hurd
et al., 2008; Bollaerts et al., 2009; Delhalle et al., 2009; Bollaerts et al., 2010). Data in
37

pre harvest are needed in order to identify possible high shedding pigs either at the farm or at
lairage (Hurd et al., 2001c; Hurd et al., 2002; Rostagno et al., 2003). In addition, during lairage
pigs are exposed to temporal and spatial variations in concentrations of Salmonella in the pen
environment (O'Connor et al., 2006a; Boughton et al., 2007). Quantitative data pre harvest can
be used to identify animals or environments that can be a potential source of Salmonella.
Quantification of bacterial load can be used to identify the contamination pressure in
different stages of pre harvest and harvest; to implement and to test the effectiveness of control
at the farm or slaughter (Fravalo et al., 2003; Boughton et al., 2007). Furthermore, quantitative
data on numbers of organisms being shed by pigs are necessary to better model the transmission
of Salmonella in pre harvest settings (Lanzas et al., 2011). There is a lack of quantitative data
either during pre harvest or harvest, that might contribute to the large level of uncertainty in
mathematical models. Current data on the quantity of Salmonella shed by pigs in feces are
mainly based on experimentally infected pigs (Wood and Rose, 1992; Fedorka-Cray et al., 1994;
Gray et al., 1996a; Osterberg and Wallgren, 2008; Scherer et al., 2008; Osterberg et al., 2009).

Techniques available to quantify: MPN, MRSV, real time PCR
Methods to quantify Salmonella can be divided into culture dependent and
culture independent approaches (Malorny et al., 2008). Traditionally, quantification of
Salmonella in fecal and carcass samples has been based on two culture dependent
methodologies: most-probable number (MPN) and direct plating (Malorny et al., 2008).
The MPN is based on culture of three to five tube replicates that are prepared from
10 fold serial dilutions and tested using horizontal culture methods (Malorny et al., 2008;
38

Jasson et al., 2010). MPN has been used to quantify the Salmonella load in fecal material of
experimentally infected pigs (Wood and Rose, 1992; Fedorka-Cray et al., 1994; Gray et al.,
1996a), contaminated slurry (Hurd et al., 2001b) and contaminated lairage environments
(O'Connor et al., 2006a; Boughton et al., 2007). The MPN is best used in the presence of low
bacterial concentrations (<50 CFU/g for food samples) and allow for enrichement (Malorny et
al., 2008; Jasson et al., 2010; Krämer et al., 2011). This type of methodology is time
consuming, labor intensive and costly; therefore it can be an impediment to use in studies with
2

large numbers of samples. In the case of samples with high concentrations of bacteria (10 to
3

10 CFU/g or more) quantities can be more readily estimated by direct plating. However, the
ability to quantify bacteria using direct plating depends also on the sample matrix and
background flora (Malorny et al., 2008; Jasson et al., 2010; Krämer et al., 2011).
Modified culture methods have been developed to quantify Salmonella in pig feces and
on pork carcasses; for example a semi quantitative approach based on modified semisolid
Rappaport Vassiliadis (MRSV) (Jensen et al., 2003; Osterberg and Wallgren, 2008; Osterberg
et al., 2009) and mini MSRV MPN technique (Fravalo et al., 2003; Fablet et al., 2006; Krämer
et al., 2011) have been described. In the MRSV method, samples are serially diluted in
enrichment media, followed by plating on semi solid MSRV agar plates. The semi quantitative
result is recorded using scores (Osterberg and Wallgren, 2008; Osterberg et al., 2009). The
mini MSRV method is based on miniaturization of dilution, pre enrichment and selective
enrichment on MSRV steps in a 12 micro well plate. The automation of the method and the
minimal amount of media used are advantages of this method compared to the traditional MPN
(Fravalo et al., 2003; Fablet et al., 2006; Krämer et al., 2011).
39

Overall, quantitative methods based on culture are time consuming (3 7 days), labor
intensive and costly. The detection limit is variable, depending upon on the method. In general
2

the limit of detection ranges between 1.8 to 10 CFU/g depending on the laboratory (Malorny et
al., 2008; Jasson et al., 2010). For these reasons, quantitative methods should not be used to
detect Salmonella positive samples, rather only to quantify. This is important considering that
asymptomatic carrier pigs shed Salmonella in feces in low concentrations (Osterberg and
Wallgren, 2008; Scherer et al., 2008; Osterberg et al., 2009), and these might not be detectable
by traditional quantitative culture methods.

Quantitative real time PCR
Culture independent methods have been developed in order to reduce the cost and time
of processing (Malorny et al., 2008; Jasson et al., 2010; Krämer et al., 2010; Löfström et al.,
2011). Among the culture independent methods, quantitative real time polymerase chain
reaction (PCR) methods have been used to quantify Salmonella in food matrices, pig feces and
pig carcasses (Abley et al., 2005; Malorny et al., 2008; Park et al., 2008; Jasson et al., 2010;
Krämer et al., 2010; Löfström et al., 2011).
PCR is based on amplification of a specific DNA sequence during a short period of time
(Heid et al., 1996; Levin, 2009). Real time PCR quantifies gene copy numbers by measuring
the accumulation of a specific or non-specific fluorescent probe. The fluorescent signal
generated by the probe is directly proportional to the amount of PCR product generated.
Quantification is based on the exponential increase of the initial DNA amount and determined

40

by the number of PCR cycles performed to threshold values (Heid et al., 1996; Malorny et al.,
2008).
The genes targeted in real time PCR pig samples differ by study and matrix. Real time
PCR protocols, targeting the ttrRSBCA locus (Krämer et al., 2011; Löfström et al., 2011) and
invA gene (Abley et al., 2005; Guy et al., 2006) have been developed to quantify Salmonella in
pig carcass and fecal samples. The invA gene is located on Salmonella Pathogenecity Island 1
(SPI1) and is fundamental for epithelial invasion (Galan, 1996). The invA gene is a unique
sequence common to 626 Salmonella strains (Rahn et al., 1992). Hoorfar et al. (2000)
developed a 5’ nuclease TaqMan assay for identification of Salmonella enterica; the PCR
oligonucleotides primers and probe had target the invA gene, in order to amplify a DNA
sequence of 119 base pairs (bp). The Salmonella probe was labeled with 6-carboxyfluorescein
(FAM) (reporter dye) and 6-carboxytetramethylrhodamine (TAMRA) (quencher dye). This
TaqMan assay was demonstrated to identify 110 Salmonella strains (Hoorfar et al., 2000).

Enrichment and no enrichment prior to real time PCR
A pre enrichment step before the PCR assay is part of several real time PCR protocols
for either detection or quantification of Salmonella on pig carcasses (Bohaychuk et al., 2007;
Malorny et al., 2008; Krämer et al., 2010) or feces (Malorny and Hoorfar, 2005; Wilkins et al.,
2010b).
The theoretical analytical sensitivity of PCR is one positive microbial cell (live or dead)
per PCR reaction (e.g. 1 genome/PCR). Due to sample preparation (e.g. PCR inhibition) and
small volume (approximately 10 l extracted DNA) used in this molecular technique’s
41

3

4

diagnostic sensitivity is 10 10 cells /ml or gram of sample (Malorny and Hoorfar, 2005;
Malorny et al., 2008; Jasson et al., 2010). Enrichment of the samples is required when the levels
3

of Salmonella are low in order to raise the concentration to a level approximately 10 to 10

4

cells per ml of enriched broth (Malorny and Hoorfar, 2005). In addition, the enrichment should
inhibit the growth of background flora and recover or revitalize stressed or damaged salmonellae
(Malorny and Hoorfar, 2005; Malorny et al., 2008; Krämer et al., 2011). The selection of
enrichment broth can affect the detection limit of the assay; thus, enrichment protocols should be
standardized to better compare results (Malorny and Hoorfar, 2005; Malorny et al., 2008).
Malorny et al., (2008) recommend a non selective broth (e.g. BPW) as pre enrichment when
the concentration is less than 500 cells per gram or ml either in food or environmental samples
(Malorny et al., 2008). Lately, Kramer et al. (2011) developed a combined enrichment and
real time PCR method for enumeration of salmonellae from pig carcasses, that allows
enumeration of low numbers of Salmonella in cork borer samples with a short time period of
enrichment (8h) (Krämer et al., 2011). On the other hand, the use of an enrichment step in
quantitative PCR prevents the ability to relate the real numbers of organisms in the analyzed
sample and the enumeration by PCR (Jasson et al., 2010); however, relative comparisons within
study are still possible.
Several studies described direct quantification in carcasses (pig, beef and chicken) (Guy
et al., 2006; Wolffs et al., 2006; Wolffs et al., 2007; Löfström et al., 2011), food samples
(Fukushima et al., 2007; Cheng et al., 2009; Elizaquivel et al., 2011) and fecal material (horses
and pigs) (Abley et al., 2005; Harris et al., 2007; Pusterla et al., 2010) using real time PCR.
The quantification limit varies, depending on matrix, assay and pre processing.
42

Separation and DNA extraction of the target cells for quantitative analysis without
enrichment can be processed either by a single or two step approach prior to the real time PCR
(Malorny et al., 2008). In single step protocols, DNA extraction is processed in a lytic buffer
solution (Malorny et al., 2008). In a two step approach, DNA extraction is preceded by
methods such as filtration (Wolffs et al., 2006; Fukushima et al., 2007), flotation (Fukushima et
al., 2007; Wolffs et al., 2007; Löfström et al., 2011), sedimentation (Fukushima et al., 2007) and
pre treatment of the matrix with ethidium monoazide (Guy et al., 2006). These pre extraction
steps are applied in order to separate, concentrate or detect viable bacteria cells before the DNA
extraction itself (Malorny et al., 2008). The quantification performance of the PCR can be
affected by type of DNA extraction protocol such as, protocol efficiency, the nature of matrixes,
and presence of PCR inhibitors (Jensen and Hoorfar, 2002; Malorny and Hoorfar, 2005; Klerks
et al., 2006; Levin, 2009). In on study the QIAamp

DNA stool kit showed better

performance, calculated as Ct (threshold cycle) value of real time PCR in pig fecal samples
spiked with S. Typhimurium; on the other hand for pork samples Charge Switch

gDNA

minibacteria was more suitable (Lettini et al., 2011).

Detection limit of real time PCR
Few studies have enumerated Salmonella in swine fecal samples using real-time PCR
without enrichment. Only two studies have reported the use of real time PCR without
enrichment in swine fecal material. Abley et al. (2005) evaluated the performance of a
real time PCR assay on pig fecal samples spiked with Salmonella enteritidis (ATCC 13076).
43

Salmonellae were inoculated in negative fecal samples with final concentrations ranging from
1

8

10 to 10 CFU/gram. This assay could sporadically detect concentrations as low as 10 copies
3

per gram and quantify at concentrations greater than 10 copies per gram of feces (Abley et al.,
2005). A higher limit for quantification was obtained by other authors (Harris et al., 2007).
1

8

Salmonella free fecal samples were spiked with ten fold dilutions from 10 to 10 CFU. The
4

assay could quantify Salmonella as low as 10 CFU in feces. The estimated concentration by
real time PCR was within one log10 when compared to MPN. In addition, the limit of detection
of real time PCR for artificially contaminated concrete (using a hydrosponge to sample the
4

concrete) was 10 CFU/ hydrosponge (Harris et al., 2007).
In summary, quantitative real-time PCR is an alternative to the traditional quantitative
culture-dependent method. It allows enumeration of Salmonella in a large number and variety
of samples in an efficient time cost and automated method (Malorny et al., 2008; Elizaquivel et
al., 2011; Löfström et al., 2011). However, it is limited due a small volume of target sample
used in the PCR assay. The lower limit for quantification without any enrichment is
2

4

approximately 10 to 10 cells per gram (or milliliter) and can depend on the matrix and
protocol (Abley et al., 2005; Harris et al., 2007; Malorny et al., 2008; Jasson et al., 2010). This
limitation should be considered in decisions to apply this methodology for numeration of
Salmonella in specific matrices as food (Malorny et al., 2008; Jasson et al., 2010),
environmental samples from lairage (Harris et al., 2007) or feces from naturally infected pigs
(Fravalo et al., 2003; Fablet et al., 2006), or any sample type where expected concentrations are
44

below the detection limit. In addition, to those factors previously mentioned, other factors can
affect the performance of real time PCR. These includes the standard curve setup, the type of
sampling and sampling technique, homogenization of the sample, and the type of cells in
samples (dead or viable versus stressed) (Malorny et al., 2008).
In natural settings, the majority of pigs shed low concentrations of bacteria, below the
quantitative limit of q PCR. In a quantitative study using the mini MSRV MPN technique ,
86% of swine fecal samples had less than 200 organisms per gram (Fravalo et al., 2003). Using
the same technique, concentrations of 2.4 to 350 MPN per gram of feces were reported in
pooled fecal samples of finishing pigs in a study on French farms (Fablet et al., 2006).
Quantitative studies at lairage have reported variable and relatively low bacterial loads, with
2

median pen floor concentrations ranging from 1.8 11.5 organisms/100cm (Boughton et al.,
2007) and 457 1071 organisms/ml of slurry (O'Connor et al., 2006a). However, the differences
found between lairage and individual pigs are not directly comparable because of different
sampling methodologies and the likely cumulative contamination of lairage. In experimental
studies pigs shed low concentrations within a few days after challenge that decrease over time to
levels that are not detectable (Scherer et al., 2008).

45

EPIDEMIOLOGY OF SALMONELLA IN SWINE
Prevalence of Salmonella in swine in the US
Salmonella is wide spread in livestock production systems. In a multi state study
(Tennessee, North Carolina, Alabama, California and Washington) swine production systems
had the highest proportion of Salmonella positive samples (57.3%), followed by dairy (17.9%),
poultry (16.2%) and beef cattle (8.5%) (Rodriguez et al., 2006).
In the US, there are two types of surveillance programs for Salmonella in swine, one at
the slaughterhouse by the Food Safety and Inspection Service (FSIS) (sampling carcasses) and
the other by the National Animal Health Monitoring System (NAHMS) (sampling on farm). The
prevalence of Salmonella at the slaughterhouse is variable, depending on the study and year, with
a relative stable proportion of positive carcasses around 2.5 % the last three years (USDA-FSIS,
2010). The proportion of Salmonella positive hog market carcasses reported by FSIS has
decreased from 6.25% in 2000 to 2.28% in 2009 (USDA-FSIS, 2010). At the farm level, based
on the 2006 NAHMS survey, the proportion of positive farms was 52.6% and the proportion of
positive fecal samples was 7.2%. This is an increase relative to the 2000 NAHMS data (34.7%
and 6.6%, respectively) (Bush et al., 2002; USDA-APHIS, 2009). The top five serovars in the
2000 and 2006 NAHMS surveys were: S. Derby, S. Typhimurium var. Copenhagen , S. Agona ,
S. Anatum and S. Heidelberg (Bush et al., 2002; USDA-APHIS, 2009).
In the US, no national Salmonella control program in swine production has been adopted
in contrast to several European countries (Hautekiet et al., 2008; Abrahantes et al., 2009;
Baptista et al., 2010b; Snary et al., 2010; Merle et al., 2011). Several epidemiological studies
have been conducted to estimate prevalence and risk factors involving a smaller number of states
46

and herds. Prevalence estimates have been based either on culture of lymph nodes (Carlson and
Blaha, 2001; Gebreyes et al., 2004; Bahnson et al., 2005; Bahnson et al., 2006a; Bahnson et al.,
2006b), fecal culture of individual pigs (Davies et al., 1997b; Davies et al., 1998; Funk et al.,
2001a; Funk et al., 2001b; Barber et al., 2002; Gebreyes et al., 2004; Hurd et al., 2004; Bahnson
et al., 2006b; Gebreyes et al., 2006; Dorr et al., 2009; Wang et al., 2010; Rostagno et al., 2011),
or serology (serum or meat juice) (Funk et al., 2005; O'Connor et al., 2006b; Gebreyes et al.,
2008; McKean and O'Connor, 2009). There is a high variability in the prevalence estimates; the
herd prevalence ranges from 64% to 100% , and the individual fecal prevalence can be as low as
0% and high as 84% (Davies et al., 1997b; Davies et al., 1998; Carlson and Blaha, 2001; Funk et
al., 2001a; Funk et al., 2001b; Barber et al., 2002; Gebreyes et al., 2004; Hurd et al., 2004;
Bahnson et al., 2005; Funk et al., 2005; Bahnson et al., 2006a; Bahnson et al., 2006c; Gebreyes
et al., 2006; O'Connor et al., 2006b; Gebreyes et al., 2008; Dorr et al., 2009; McKean and
O'Connor, 2009; Wang et al., 2010; Rostagno et al., 2011). Generalization of results among the
studies is challenging due to differences in study design (cross-sectional versus longitudinal),
sampling strategy, targeted population (production stage), and diagnostic test (culture of feces
versus lymph nodes, serology).
Prevalence estimates based on bacteriological culture of the tissues or fecal material at
slaughter may (Bahnson et al., 2005; Bahnson et al., 2006b) or may not (Gebreyes et al., 2004)
be representative of the infection status at the farm. Numerous studies have reported higher
prevalence of Salmonella at slaughter (cecal contents or lymph nodes) compared to fecal
sampling at the farm (Hurd et al., 2001c; Hurd et al., 2002; Gebreyes et al., 2004; Dorr et al.,
2009). In addition, isolation of different serovars at slaughter as compared to those isolated at
the farm suggest new infections acquired between the farm and slaughter (Hurd et al., 2001c;
47

Gebreyes et al., 2004; Bahnson et al., 2005; Dorr et al., 2009). Despite the difference of
sampling between farm and slaughter; this discrepancy suggests that transport and lairage might
result in increased shedding of bacteria and acquisition of new infections (Hurd et al., 2001c;
Hurd et al., 2002; Gebreyes et al., 2004; Dorr et al., 2009). Therefore, Salmonella positive
samples taken at slaughter might be a result of exposure or activation of infections at the farm,
transportation and slaughter. Knowing pigs harboring Salmonella are the main reservoir and
consequently being the source of infection for non-infected pigs either at farm, transportation or
lairage.
Based on epidemiological studies, the within-herd fecal prevalence on finishing swine
farms in North America ranges from 2% to 84% and the proportion of positive samples ranges
from 2% to 38% (Davies et al., 1997b; Funk et al., 2001b; Bush et al., 2002; Hurd et al., 2004;
Rajic et al., 2005; Bahnson et al., 2006b; Gebreyes et al., 2006; Rajic et al., 2007b; Farzan et al.,
2008a; Poljak et al., 2008; Dorr et al., 2009; Wang et al., 2010; Wilkins et al., 2010a; Rostagno
et al., 2011). The most commonly isolated serovars in finishing fecal samples are: S.
Typhimurium, S. Typhimurium var Copenhagen, S. Derby, S. Agona, S. Mbandaka, S. Infantis,
S. Muenster (Davies et al., 1997b; Davies et al., 1998; Funk et al., 2001b; Gebreyes et al., 2004;
Rajic et al., 2005; Farzan et al., 2008a; Farzan et al., 2008b; Dorr et al., 2009; Wang et al., 2010;
Wilkins et al., 2010a). Several reasons can explain this variability in prevalence estimates such
as: 1) differences in swine production systems between countries, states or regions, 2) study
design (cross-sectional versus longitudinal), 2) type of sampling (individual versus pooled), 3)
type of diagnostic test culture methods), 4) time of sampling, and 5) intermittent shedding.

48

Cross sectional versus longitudinal studies
Importance of longitudinal studies
Most of the epidemiological studies that have provided estimates of prevalence and risk
factors of salmonellosis in swine have been cross-sectional. Cross-sectional studies are
’snapshots’ of the population status with the respect to disease or exposure variables, in which
all the information refers to the same point in time. The estimation of prevalence depends upon
incidence and duration of the disease (Dohoo et al., 2010). In infectious diseases with
asymptomatic carriers and intermittent shedding, such as salmonellosis, prevalence is the most
common measure of disease frequency described in epidemiologic studies. This is because it is
often not possible to distinguish a new infection from recurrence of a previous infection. In
addition, due to the use of imperfect diagnostic tests, such as fecal culture, with low to moderate
sensitivity, apparent prevalence is the frequency measure described in the majority of the
studies. Few studies have reported the true prevalence, adjusted based on assumptions of
diagnostic test performance (sensitivity and specificity) (Funk et al., 2000) or herd sensitivity
(Farzan et al., 2008b). Therefore, considering the characteristic chronic/intermittent shedding
and the use of an imperfect test, one point in time estimate may not be adequate to determine
farm or pig Salmonella status. Epidemiologic studies based on point estimates of the
prevalence may result in misclassification of both farms and pigs (Funk et al., 2001b; Rajic et
al., 2005; Rajic et al., 2007a; Farzan et al., 2008b). Cross sectional studies are not the best to
assess the time variant risk factors associated with diseases (Dohoo et al., 2010). Longitudinal
studies allow investigation of time variant risk factors associated with diseases.

49

A number of studies have been conducted longitudinally either at the farm level (van der
Wolf et al., 2001a; Lo Fo Wong et al., 2004b; Rajic et al., 2005; Rajic et al., 2007a; Farzan et
al., 2008b; Rostagno et al., 2011), at the cohort level (Merialdi et al., 2008; Dorr et al., 2009;
Vigo et al., 2009) or at the pig level (Funk et al., 2001b; Beloeil et al., 2003; Kranker et al.,
2003; Nollet et al., 2005a; Merialdi et al., 2008; Vigo et al., 2009). For the purpose of this
review, longitudinal studies at the farm level are defined as repeated sampling of the farm by
sampling different groups of pigs; cohort level represents repeated sampling of the same group
of pigs; and pig level sampling represents repeated sampling of the pigs individually.
Longitudinal studies at the farm level based on seroprevalence (Lo Fo Wong et al.,
2004b; Rajic et al., 2007a) or fecal prevalence (Rajic et al., 2005; Rajic et al., 2007a; Farzan et
al., 2008b) have shown high variability of farm status over time. Raijic et al. (2005) conducted a
large scale longitudinal study in ninety Alberta swine finishing farms where pooled fecal and
environment samples were collected three times over a five month period. The authors reported
that 63.3% of finishing farms had no Salmonella positive samples on one visit but had one or
more positive samples on other visits (Rajic et al., 2005). A significant variability over time in
farm status was presented when those farms were categorized based on seroprevalence (Rajic et
al., 2007a). In a five year study conducted by Farzan et al. (2008) in 113 Ontario swine farms,
there was variability of fecal prevalence and isolated serovars during the study period (Farzan et
al., 2008b). A limitation of this study was that different sampling strategies, type of sampling
(individual versus pools) and culture protocols were used across the years, which may in part
contribute to the variability in the results (Farzan et al., 2008b). Therefore, classifying farms as
Salmonella positive or Salmonella negative based on a single sampling and using diagnostic
tests of poor sensitivity is likeky to lead to misclassification of the true farm status (Funk et al.,
50

2001b; Rajic et al., 2005; Rajic et al., 2007a).

Longitudinal studies at the pig level
Longitudinal studies at the pig level have reported time variability of fecal shedding
associated with cohort or batch of pigs (Funk et al., 2001b; Beloeil et al., 2003; Kranker et al.,
2003; Nollet et al., 2005a). Funk et al. (2001) conducted a longitudinal study in 2 multi site
production systems, where individual fecal samples were collected from sows (gestation and
pre-wean) and pigs (piglets pre weaned to finishing, sampled six times) in five cohorts of pigs.
There was high variability of Salmonella prevalence and serovar distribution within cohorts
over time, and among cohorts within systems (Funk et al., 2001b). Finishing pigs were sampled
3 times approximately monthly; there was no uniform trend for prevalence during this phase, as
some cohorts decreased and others increased over the finishing phase (Funk et al., 2001b). In
contrast, according to one Belgium farrow to finish farm study conducted by Beloil et al.
(2003), the individual fecal and environmental prevalence (pen swabs) were higher in the first
third of the finishing phase (Beloeil et al., 2003). In another longitudinal study by Kranker et al.
(2003), in three Danish farrow-to-finish herds, the overall fecal prevalence reached a peak at 60
days of age (nursery) and decreased over time during the finishing phase. However, a marked
variation was observed between herds and cohorts (Kranker et al., 2003). Nollet et al. (2005)
conducted a study in 3 cohorts in 3 farrow to finish herds in Belgium, pigs were followed up
from weaning through the finishing phase (sampled between 5 to 6 times during the finishing
phase). Increasing prevalence was observed when the pigs were moved to the finishing unit in
one herd, and in the other herd two peaks in prevalence occurred after the pigs were moved to
51

the growing and finishing unit, although the prevalence subsequently decreased during the
finishing phase (Nollet et al., 2005a). Similarly, Vigo et al. (2009) observed increased
Salmonella shedding when pigs were placed in finishing units (Vigo et al., 2009). It was
suggested by the authors that the increased prevalence after moving to a new facility could be
due to the stress caused by transportation, comingling with new pigs, changes in feed type and
exposure to residual contamination at the new location (Nollet et al., 2005a; Vigo et al., 2009).
The distribution of serovars varies among the studies. The Kranker et al. (2003) study
took place on 3 farms known to be infected with S. Typhimurium (Kranker et al., 2003), while
in the Beloeil et al. (2003) study two serovars (S. Typhimuirium and S. Brabdenburg) were
isolated during finishing phase (Beloeil et al., 2003). On the other hand, a wider diversity of
serotypes were found by Funk et al. (2001) and Nollet et al. (2005), different serovars were
identified within same system, cohort, pen or pig (Funk et al., 2001b; Nollet et al., 2005b). The
diverse serovars found in those studies can be indicative of multiple infections on the farm or
introduction of new strains.
Although temporal variability of Salmonella shedding is generally observed, there is an
inconsistency of prevalence trend along the finishing period. Some studies have reported a
decrease in fecal prevalence (Beloeil et al., 2003; Kranker et al., 2003; Nollet et al., 2005b; Vigo
et al., 2009; Molla et al., 2010). Other studies demonstrated both increases and decreases,
depending on cohort (Funk et al., 2001b), or an increase as the pig got older (Dorr et al., 2009).
It is unclear what factors contribute to such differences. Poljak et al. (2008) suggested that the
association between the pig weight and Salmonella shedding can be explained due to correlation
between pig weight, age and health status. Management factors such as changes in feed and
in feed antibiotics, stress caused by transport, comingling pigs and stocking density, variation of
52

environmental temperature in barn and housing conditions might affect the Salmonella shedding
of finishing pigs (Nollet et al., 2005a; Funk et al., 2007; Hautekiet et al., 2008; Poljak et al.,
2008; Vigo et al., 2009). Indeed, the variation in prevalence throught the finishing phase might
be due to management factors that have not been explored during the majority of the
epidemiologic studies.
There are major study design differences that can affect the estimation of prevalence and
isolated serovars of the previous studies; these includes number of farms, type of farm
(multi site versus farrow to finishing), sampling frequency (monthly versus weekly), type of
sampling (10 gram feces versus rectal swabs) and serovars present (one versus several serovars).
Nevertheless, all of the studies found a high variability of Salmonella shedding over time either
at the cohort or pig level. Longitudinal studies with repeated sampling of farm, cohorts, and
pigs are needed to investigate the potential time variant risk factors (Funk et al., 2001b; Poljak
et al., 2008). Furthermore, longitudinal studies at the individual level based on bacteriological
fecal culture should be performed in order to investigate the dynamics of Salmonella infection
(e.g. age, duration of infection, and disease transmission patterns) in swine over time. The
dynamics of Salmonella infection is likely to be complex. The duration of shedding in naturally
infected pigs has been estimated to be between 7 to 101 days, with a mean time 18 or 26 days,
depending on the assumptions and based on monthly sampling (Kranker et al., 2003). Another
study showed that the majority of pigs were detected to be shedding once based on weekly
samplings (Beloeil et al., 2003). Thus, monthly sampling may underestimate new infections or
status of individual pigs.

53

Herd risk factors
Numerous studies worldwide have focused on evaluation of fecal shedding and risk
factors during the finishing phase (Davies et al., 1997a; Davies et al., 1997b; Davies et al.,
1998; van der Wolf et al., 1999; Stege et al., 2000a; Funk et al., 2001a; Funk et al., 2001b; Lo
Fo Wong et al., 2003; Beloeil et al., 2004; Gebreyes et al., 2004; Hurd et al., 2004; Rajic et al.,
2005; Bahnson et al., 2006b; Gebreyes et al., 2006; García-Feliz et al., 2007; Rajic et al., 2007b;
Farzan et al., 2008a; Farzan et al., 2008b; Magistrali et al., 2008; Poljak et al., 2008; Dorr et al.,
2009; Garcia-Feliz et al., 2009; Lomonaco et al., 2009; Wang et al., 2010; Wilkins et al., 2010a;
Rostagno et al., 2011).
The aforementioned studies were focused on assessing the farm level prevalence in a
large number of finishing farms. Differences in management of the farms might contribute to
variation of Salmonella prevalence. In a cross sectional study on 80 farms in Ontario done by
Polkjak et al. (2008), the farm level variance contributed the largest proportion to the total
variance of Salmonella shedding of pigs, followed by the pen (Poljak et al., 2008). In this study
the majority of the variables associated with Salmonella shedding were farm related variables
(e.g. feed; disinfection, closed barn, number of pigs); no time dependent variables were
assessed. Thus, the level of highest variability might change to another level when variables at
other levels are accounted for in multilevel analysis. For example, Funk et al. (2007) reported
that the individual pig fecal sample was the level which contributed the most to the odds of
fecal sample being Salmonella positive when compared pigs treated with and without
subtherapeutic chlortetracycline (Funk et al., 2007).

54

The herd level risk factors associated with Salmonella prevalence in pig herds are
tipically related to introduction of the pathogen to the herd, transmission among pigs (or within
herd), and the survival of Salmonella in the individual pig (Zheng et al., 2007; Hautekiet et al.,
2008). The introduction and maintenance of the pathogen in the herd environment involves
factors associated with biosecurity and herd management such as type of production, hygiene
and presence of vectors (domestic and wildlife animals). The transmission among the pigs and
survival of Salmonella in the host includes herd and health management factors such as feed
type, concomitant diseases and use of antibiotics (Funk and Gebreyes, 2004; Zheng et al., 2007;
Hautekiet et al., 2008; Fosse et al., 2009).
Biosecurity measures include a set of practices to avoid the introduction of new
infections, to prevent the spread of diseases and mitigate the persistence of pathogens on farm
(Twomey et al., 2010). Those measures include among others: all in all out flow management,
cleaning and disinfection, personnel hygiene, access to the herd by visitors, and contact and
presence of domestic animals and wildlife (Funk and Gebreyes, 2004; Fosse et al., 2009;
Baptista et al., 2010a; Twomey et al., 2010). Recently, two studies, one in Portugal and the
other in the U.K., investigated the association between biosecurity measures and Salmonella
infection. Herds with poor biosecurity measures were more likely to be Salmonella positive
compared with herds with good biosecurity, suggesting that multiple biosecurity measures
should be applied simultaneously in order to prevent the introduction and spread of Salmonella
(Baptista et al., 2010a; Twomey et al., 2010). Nevertheless, farmers might be less receptive to
implement those procedures to control foodborne zoonoses, particularly if the cost is high
(Fraser et al., 2010).

55

All in all out flow management has been associated with decreased Salmonella
prevalence on swine farms (Lo Fo Wong et al., 2004a; Farzan et al., 2006; Hautekiet et al.,
2008; Cardinale et al., 2010). This type of production system decreases the risk of
cross contamination because it allows segregation of age groups and ‘down’ time between
batches for cleaning and disinfection (Funk and Gebreyes, 2004). In a recent review by Fosse et
al. (2009), clean downtime (‘empty and clean’) periods of less than three, six and seven days in
fattening, farrowing and post weaning stages, respectively, were associated with higher risk of
presence of Salmonella (Fosse et al., 2009).
Although it is generally accepted that cleaning and disinfection of the facilities and
equipment decreases Salmonella environmental contamination; those practices have been
inconsistently associated with a decrease in Salmonella prevalence on swine farms (Funk and
Gebreyes, 2004; Fosse et al., 2009). On one hand, a higher risk for Salmonella (Cardinale et al.,
2010) and other enteric diseases (Pearce, 1999) in the absence of disinfection of the farrowing
rooms or between batches of pigs, respectively, was observed. In addition, disinfection between
batches was associated with lower seroprevalence in Belgium market pigs (Hautekiet et al.,
2008). On the other hand, a study of 80 Ontario farms found that the increased frequency of
disinfection and washing with cold water were positively associated with Salmonella positivity
measured by bacteriological culture at the farm and pen level (Poljak et al., 2008). Identical
findings were reported by van der Wolf et al. (2001); the omission of disinfection of the rooms
after pressure washing, was associated with lower Salmonella seroprevalence (van der Wolf et
al., 2001b). In contrast, in 89 Alberta swine finishing farms, no significant difference in
Salmonella shedding was observed among farms that did not clean or only scraped the pens or
used pressure washing with or without disinfection between batches (Rajic et al., 2007b).
56

Although the findings are contradictory, it is well known that the cleaning and disinfection of the
barn can reduce the Salmonella contamination level, but does not completely eliminate it.
Several studies have shown residual Salmonella post disinfection of barns and trucks (Funk et
al., 2001b; Mannion et al., 2007; Dorr et al., 2009; Zewde et al., 2009b). In fact, residual
environmental Salmonella contamination of the room was identified as a risk factor for
Salmonella status of fattening pigs (Fablet et al., 2003; Beloeil et al., 2004; Beloeil et al., 2007).
In addition, differential efficacy of cleaning and disinfection protocols have been reported
(Mannion et al., 2007; Dorr et al., 2009); disinfectants (Mueller-Doblies et al., 2010) and
increased antimicrobial Salmonella resistance has been described when certain biocides were
applied in swine barns (Zewde et al., 2009a). Further information is needed to evaluate the
efficacy of cleaning and disinfection protocols practices on Salmonella control in swine barns.
Other personnel hygiene practices such as the presence of sanitary facilities at the entrance to
facilities (e.g. changing room and toilet in farm) (Funk et al., 2001a; Lo Fo Wong et al., 2004a;
Hautekiet et al., 2008), changing clothes or providing protective clothes and boots before
entering and leaving the facilities (Beloeil et al., 2007; Rajic et al., 2007b; Hotes et al., 2010),
and washing hands before handling pigs (Lo Fo Wong et al., 2004a) have been associated with a
lower risk for Salmonella infection (seroprevalence and bacteriological culture). In addition, the
use of disinfectant foot baths has been associated with a lower risk for Salmonella infection
(Hautekiet et al., 2008; Twomey et al., 2010).
Humans, domestic and wild animals (e.g. dogs, cats, rodents and birds) are important
vectors for the spread of Salmonella (Berends et al., 1996; Funk and Gebreyes, 2004; Fosse et
al., 2009). Several studies have found an increased risk for Salmonella with increasing number
of employees/visitors or personnel visits (Funk et al., 2001a; Cardinale et al., 2010). There was
57

an increased likelihood of detection of Salmonella with the presence of animals (e.g. birds,
rodents, cats) other than pigs on the farm (Harris et al., 1997; Funk et al., 2001a; Cardinale et al.,
2010). Protective features of the barn to avoid contact with animals (e.g. birds) such as fence
enclosed pig facilities or closed barns were found to be protective in several studies (Beloeil et
al., 2007; Poljak et al., 2008; Cardinale et al., 2010).
Housing has been recognized as a potential source of Salmonella (Letellier et al., 1999;
Funk and Gebreyes, 2004; Gotter et al., 2011). Facilities allowing nose to nose contact
between contiguous pens was associated with higher Salmonella prevalence in European (Lo Fo
Wong et al., 2004a) and Canadian (Wilkins et al., 2010a) farms. Herds with fully slatted floors
were less likely to be Salmonella positive when compared with other types of flooring (e.g.
partially slatted or straw) (Davies et al., 1997b; Nollet et al., 2004; Hotes et al., 2010; Twomey
et al., 2010). Conversely, van der Wolf et al. (2001) found no risk associated with different
types of floors (van der Wolf et al., 2001b). In general, it is accepted that slatted floors decrease
pig contact with fecal material, resulting in decreased fecal oral transmission (Funk and
Gebreyes, 2004).
Several epidemiological studies have found an association between high stocking density
and Salmonella prevalence. Funk et al. (2001) reported that higher space allowance (more than
2

0.75m /pig) was associated with reduced Salmonella fecal prevalence (Funk et al., 2001a).
However, the same research group found no difference between two levels of stocking density
2

2

(0.60 m /pig versus 0.74 m /pig) on Salmonella fecal prevalence or antimicrobial resistance in a
field trial study (Funk et al., 2007). More recently, Hautekiet et al. (2008) described higher risk
of having high Salmonella seroprevalence when floor space per pig decreases in the fattening
58

period (Hautekiet et al., 2008). The association between the stocking density and Salmonella is
not clear. The increased stocking density might increase pig to pig contact, and consequently,
transmission. In addition, the stress caused by higher density might decrease the host immune
defenses and make pigs prone to new infections (Funk and Gebreyes, 2004; Funk et al., 2007).
The stocking density might also be related to batch size, as Beloeil et al. (2007) have reported a
higher risk for Salmonella seroconversion prevalence when the number of pigs in a fattening
room increased by 10 pig increments (Beloeil et al., 2007).
Herd size has been inconsistently associated with Salmonella prevalence; some studies
show a higher risk in large herds (Carstensen and Christensen, 1998; Kranker et al., 2001;
Hautekiet et al., 2008; Garcia-Feliz et al., 2009; Benschop et al., 2010) while others show
inconsistent in small herds (van der Wolf et al., 2001b; Benschop et al., 2010). The difference
among the studies might be related to biosecurity, hygiene measures and/or purchase of animals
(Zheng et al., 2007) inherent to each size herd. Actually, “farm” contributed the largest
component of model variability in a study of Ontario finishing pigs (Poljak et al., 2008),
suggesting that farm level risk factors like herd size may significantly contribute to a farm’s
Salmonella risk. Moreover, mixing batches, continuous flow, and buying pigs from more than
three suppliers increases the risk of Salmonella seroprevalence (Lo Fo Wong et al., 2004a;
Farzan et al., 2006).
The effect of feed on Salmonella in swine can be divided into two categories: feed as
source of salmonellae due to contamination and the impact of feed ingredients/structure on
Salmonella proliferation in feed and the pig gastro intestinal tract (Funk and Gebreyes, 2004).
Feed is a potential Salmonella source to pig herds (Davies et al., 2004; Funk and Gebreyes,
2004; Molla et al., 2010; Kich et al., 2011). Salmonella has been isolated from feed trucks, feed
59

mill (Fedorka-Cray et al., 1997) or pig feed (Fedorka-Cray et al., 1997; Funk et al., 2001b;
Molla et al., 2010). Feed-related serovars such as S. Cubana (Osterberg et al., 2006) and S.
Yoruba (Osterberg et al., 2001) have been associated with feed borne outbreaks of samonellosis
in pig herds as result of contamination at feed plants. Many epidemiologic studies have
demonstrated that feed composition and structure may be associated with Salmonella prevalence
in pigs. Several factors have been investigated: wet or dry diets, feed particle size and form
(pelleted diets, finely ground feed, meal), acidified diets (feed, water or both) and heat–treated
feed (Funk and Gebreyes, 2004; Zheng et al., 2007). Numerous studies have showed higher
bacteriological or serological prevalence in pig herds using dry feeding versus herds using wet
or liquid feeding (van der Wolf et al., 1999; Kranker et al., 2001; van der Wolf et al., 2001b;
Fablet et al., 2003; Beloeil et al., 2004; Bahnson et al., 2006b; Farzan et al., 2006; Benschop et
al., 2008a; Hautekiet et al., 2008; Poljak et al., 2008; Hotes et al., 2010; Twomey et al., 2010).
The liquid feed might include a fermention step, incorporation of organic acids or bio products
(e.g. whey) resulting in acidified feed (Funk and Gebreyes, 2004; Farzan et al., 2006). Herds
fed whey, either to drink or as the liquid part of the diet, were at decreased risk of being
seropositve (Lo Fo Wong et al., 2004a). Pigs fed non pelleted feed have a lower risk of being
serologically or bacteriologically Salmonella positive compared to pigs fed pelleted feed
(Kranker et al., 2001; Leontides et al., 2003; Lo Fo Wong et al., 2004a; Rajic et al., 2007b;
Poljak et al., 2008; Garcia-Feliz et al., 2009; Wilkins et al., 2010a). Although in those studies
the category non pelleted might include dry, wet feed, ground or floury feed, the authors are
unanimous in noting that pelleted feed is a risk factor for Salmonella. In addition, in a recent
systematic review by O’Connor et al. (2008) it was reported that the use of non-pelleted feed

60

shows the strongest evidence for reducing Salmonella prevalence in market finisher swine
(O'Connor et al., 2008).
Water can be a potential source of Salmonella. Salmonellae have been isolated from
water and drinkers (Barber et al., 2002; Gotter et al., 2011). In addition, water distribution
systems might impact the risk of Salmonella transmission; as an example pigs from herds with
water bowls were more likely to be Salmonella positive compared with herds with nipple
drinkers (Bahnson et al., 2006b).
Several authors investigated the effect of health status on the risk of Salmonella in swine
farms. Herds considered to have a high health status either by belonging to the Danish Specific
Pathogen Free (SPF) program or by being a member of an Integrated Quality Control (IQC)
program (Netherlands) were at lower risk of Salmonella infection (van der Wolf et al., 1999;
Kranker et al., 2001; Benschop et al., 2008a). There was an increased risk of Salmonella
shedding and seropositivity in herds with diarrhea and concurrent diseases such as Porcine
Reproductive and Respiratory Syndrome Virus (PRRSV), Porcine Respiratory Coronavirus
(PRCV), Lawsonia intracellularis and Ascaris suum (Møller et al., 1998; van der Wolf et al.,
2001b; Fablet et al., 2003; Beloeil et al., 2004; Beloeil et al., 2007). On the other hand, LoFo
Wong et al. (2004) found no association between health status and seroprevalence in European
herds (Lo Fo Wong et al., 2004a). It is unclear if there is a direct relationship between the
presence of other diseases on farms and Salmonella or if health status is a proxy of other
unknown factors in herds that could be related to the Salmonella risk.
Antimicrobials have been widely used in swine production, both to prevent or treat
diseases as well as to promote growth (Funk et al., 2006). There a lack of consistency among
studies regarding the use of antimicrobials and the risk of Salmonella shedding. The use of
61

antimicrobials such as tylosin or a combination of chlortetracycline, procaine penicillin and
sulphamethazine as growth promoters in finishing feed was associated with higher Salmonella
seroprevalence (van der Wolf et al., 2001b; Leontides et al., 2003). In addition, pigs fed
subtherapeutic chlortetracycline in the diet were at higher risk of shedding Salmonella (Funk et
al., 2007). However, Rajic et al. (2007) reported the use of antibiotics through the water in all
production phases was associated with lower farm seroprevalence (Rajic et al., 2007a). In
contrast, no association was found between Salmonella shedding and the use of antibiotics
(chlortetracycline and penicillin) in the finisher ration in an Ontario study (Poljak et al., 2008).
The antibiotics in feed might be responsible for disrupting the normal microbial flora and
consequently increasing the colonization of the gastrointestinal tract by gram negative bacteria
such as Salmonella (Funk et al., 2007; Rajic et al., 2007a). Nevertheless, differences in type of
antibiotic, route of administration, purpose and characteristics of the farm must be considered
when comparing studies. In addition, the spectrum of antibiotic resistance of the predominant
Salmonella serovar might influence the magnitude of association regarding the prevalence and
antibiotics use.

62

SEASON, ENVIRONMENT FACTORS and FOODBORNE PATHOGENS
Seasonal pattern of diseases
Many water and foodborne diseases have a seasonal pattern. Seasonality is defined as a
cyclic appearance of events over a period of time (course of a year) and in temperate latitudes is
marked by three main factors: temperature, humidity and precipitation (Naumova, 2006). “The
temporal variability in disease results of interactions between host susceptibility, periodicity in
pathogen abundance and transmissibility, and the ever changing environment that can support
or repress a host or pathogen” (Naumova, 2006). The survival and transmissibility of pathogens
are dependent on seasonal factors such as temperature, humidity and precipitation, when they
combine to creat favorable conditions (e.g. high temperature, humidity, moisture) for pathogen
proliferation and transmission (Naumova, 2006). Seasonal patterns of diseases often differs
from location to location and can change over the years (Naumova, 2006). Lately, increased
climatic variability may pose a higher risk of water and foodborne diseases at the various stages
of the food chain, from primary production through to consumption. Climate related factors
such as changes in temperature and precipitation patterns, extreme weather events, ocean
warming and acidification, can have an impact on the persistence and patterns of occurrence of
bacteria and the patterns of their corresponding foodborne diseases, animal physiology and host
susceptibility (Tirado et al., 2010). Seasonal host susceptibility is complex and not well defined
or understood (Dowell, 2001; Naumova, 2006). Seasonal physiological changes occur in many
mammalian species, which include reproductive and immunological alterations. The seasonal
physiologic cycles are linked to light and dark cycles as mediated through the duration of daily
melatonin release. Those physiologic responses might lead to higher susceptibility to
63

infections, because of an increased responsiveness of specific and nonspecific immunity,
changes in the characteristics of mucosa surfaces and the expression of epithelial receptors
(Dowell, 2001). The day light length might even have an effect on E. coli O157:H7 seasonal
shedding pattern in cattle. Cattle naturally infected with E. coli O157:H7 exhibit higher
prevalences in lighted pens (Edrington et al., 2006). On the other hand, studies with melatonin
were inconclusive regarding the physiological effect of day light length on shedding in cattle
(Edrington et al., 2008).

Seasonality of foodborne pathogens in Humans
Seasonal patterns of foodborne diseases in humans have been observed in temperate
climates. For example human illness caused by Salmonella spp and Campylobacter jejuni tends
to rise in summer and decrease in the winter (Naumova et al., 2007). Seasonal variation (or
temporal variations) of those foodborne diseases has been correlated with oscillations of several
environmental factors (Naumova et al., 2007). Among those environmental factors, ambient
temperature has been consistently associated with human salmonellosis worldwide (Bentham
and Langford, 2001; D'Souza et al., 2004; Fleury et al., 2006; Naumova et al., 2007). A linear
association between ambient temperature and the number of human cases has been reported. In
European countries, cases of salmonellosis increased about 5% to 12.5% for each one degree
increase in weekly temperature for ambient temperatures above 6 C (Kovats et al 2004). In
Canada, the log relative risk of Salmonella weekly case counts increased by 1.2% for every
degree increase in weekly mean temperature (Fleury et al., 2006). The association between the
ambient temperature and incidence of human cases does not occur simultaneously. There is a
64

lag time between the change in ambient temperature and the respective onset of disease. Time
series analysis studies of human salmonellosis in several in European countries, North America
and Australia showed that, in general, human cases of salmonellosis increased 1 6 weeks after
the peak in ambient temperature (Bentham and Langford, 2001; D'Souza et al., 2004; Fleury et
al., 2006; Naumova et al., 2007; Lake et al., 2009). In United States, the peak of daily incidence
of salmonellosis closely followed the peak in ambient temperature with a lag of 2 14 days in
Massachusetts (Naumova et al., 2007). Short-term lag times between the peak of ambient
temperature and illness could suggest that cross contamination and bacterial multiplication on
food may occur from distribution systems to the point of consumption during food preparation
(Bentham and Langford, 2001; Lake et al., 2009). Longer lag times were found in 5 Australian
cities, a positive association between mean temperature of the previous month and number of
salmonellosis notifications was found (D'Souza et al., 2004). In a Canadian study, the ambient
temperature 0 to 6 weeks prior was found to be associated with the onset of human cases
(Fleury et al., 2006). Long term lag times might suggest the ambient temperature affects
Salmonella at any point along the food chain, including at the farm, the slaughterhouse,
distribution systems or the home (Bentham and Langford, 2001; D'Souza et al., 2004; Fleury et
al., 2006; Lake et al., 2009). Lastly, we cannot ignore the fact that higher ambient temperature
during the summer may influence consumer behavior; for example, via riskier cooking practices
(e.g. barbecue) or consumption of raw foods (e.g. fruit or salads, uncooked meat) (Lake et al.,
2009). Overall, the association between the mean and highest temperature several weeks prior
the onset of the human cases suggest a hypotheses that the ambient temperature might affect the
Salmonella dynamics at the farm level. Those effects might be mediated either by creating an
environment favorable for the proliferation of bacteria in the environment and consequently
65

increasing the bacterial pressure and exposure to the livestock animals, or by increasing the
animals’s susceptibility to new infections or inducing recurrence of existing infections.

Seasonality of salmonellosis in swine
A number of studies have investigated the seasonality of Salmonella infections in swine
with mixed results. Earlier studies based on the Danish Salmonella Surveillance and Control
Program reported a seasonal pattern in Salmonella infection, with higher seroprevalence noted
during the winter and fall (Carstensen and Christensen, 1998; Christensen and Rudemo, 1998;
Hald and Andersen, 2001). However, more recent Danish studies have showed no seasonality of
Salmonella seroprevalence. The distribution of Salmonella seroprevalence in meat juice
(Danish Mix ELISA) of slaughtered pigs did not follow a consistent seasonal pattern in
a10 year time series study. The authors explained that the differences observed with respected
to the previous studies were likely due to the larger number of years included in this study.
Consequently, they infered there was no need for targeted sampling during particular times of
the year (Benschop et al., 2008b). In a second study, no seasonality was found in pooled carcass
cultures (over six year period) in Danish pig abattoirs (Baptista et al., 2010b). These findings
are in contrast with those found by Hald et al. (2001) in a five year study, which showed that
the seroprevalence of Salmonella positive slaughter pigs peaked in late winter and early fall,
suggesting that the peak in swine seroprevalence was due to new infections that occur primarily
in the late summer. In the same study, S. Typhimurium human incidence and pork prevalence
started increasing in spring and peaked in late summer. Interestingly, late summer peak
prevalence in pork appeared three to four weeks before the peak in human cases. Actually, the
66

weekly number of human S. Typhimurium cases was associated with the prevalence level in
pork samples one to five weeks before case registration (Hald and Andersen, 2001). This
suggests a potential impact of the thermal environment on Salmonella during distribution or in
primary production. Nevertheless, the seroprevalence is representative of historical exposure to
the bacteria that can occur at any point on the farm. In addition, pig Salmonella carcass
contamination might result either from the intestinal carriage of Salmonella in the pig itself or
cross contamination in slaughter. Therefore, the disparity of the findings in those studies might
be attributable to differences of unknown/unmeasured factors associated with farm and slaughter
infections and/or contamination.
Seasonal peaks of Salmonella prevalence in pigs have been identified by other research
groups in the US, UK and Belgium (Funk et al., 2001a; Hautekiet et al., 2008; Smith et al.,
2010). Finishing pigs with higher Salmonella fecal prevalence were at greater odds of having
been sampled in winter and spring in a North Carolina study (Funk et al., 2001a). Smith et al.
(2010) reported higher seroprevalence during the fall in slaughter pigs in a UK monitoring
Zoonosis Action Plan (ZAP) program (Smith et al., 2010). In contrast, Hautekiet et al. (2008)
found that sampling in summer was associated with higher seroprevalence (Hautekiet et al.,
2008).
It is evident that discrepancies in the seasonal patterns seen among various studies and
countries exist. Season is a broad concept and seasonal patterns can vary from time to time and
from region to region; consequently, patterns might easily change from country to country and
even within. Seasonal cycles of infectious diseases involve complex interaction between three
groups: pathogen appearance and disappearance, environmental changes, and host behavior
changes (Dowell, 2001). In the case of livestock production systems, the host behavior changes
67

are dependent on management factors. Moreover, management practices might themselves
exhibit seasonal variability and consequently affect the seasonality of Salmonella in swine (Hald
and Andersen, 2001). Therefore, it is possible that some unobserved factors such as
management may affect the seasonal pattern of Salmonella infection in swine. In addition,
management practices specific to production system type vary from country to country and can
contribute to different Salmonella seasonality patterns in swine. Another aspect that must be
taken into account is the type of diagnostic test used to measure Salmonella infection
(seroprevalence versus fecal culture). Salmonella infection and fecal shedding in swine might
have seasonal peaks, which eventually were not found in corresponding seroprevalence peaks,
because of antibodies persistency for long periods after the animal being infected.

Thermal environment factors associated with salmonellosis in swine
Season is characterized by environmental changes of temperature, humidity, precipitation
and wind (Dowell, 2001; Naumova, 2006). Environmental factors such as temperature, rainfall
and sunshine have been associated with Salmonella prevalence in swine. Finishing pigs exposed
to wide variation in daily temperature high were at greater risk of Salmonella shedding (Funk et
al., 2001a). In addition, large differences in long term averages in the monthly mean
temperature, as well as high actual rainfall and hours of sunshine were associated with higher
Salmonella seroprevalence in UK pigs (Smith et al., 2010). In both studies the environmental
parameters were retrieved from the closest weather station. Therefore, the environmental
parameters might not reflect the actual environment in the barns.

68

Herds that had controlled and programmed barn temperatures above upper critical values
(> 26 C, for pigs of 90 kg) had a higher seroprevalence (defined as average S/P) compared with
herds with controlled programmed barn temperatures within the thermal neutral zone (Hautekiet
et al., 2008). Herds with no temperature programming in the swine facilities (i.e. controlled
temperatures) in fattening units had the highest Salmonella seroprevalence (Hautekiet et al.,
2008). Controlled temperature and ventilation systems are important features of facilities to
keep the animals in their thermo neutral zone in order to promote production and overall swine
health. Nevertheless, controlling the environment of barns can be a challenge to keep them
within optimal temperature ranges during certain seasons. Environment factors (e.g.
temperature, rainfall and sunshine) might affect not only the host (pig) but also the sources and
the survival of the pathogen in external environment. Certain environmental factors might
support the survival and proliferation of Salmonella in the environment and consequently
increase the bacterial exposure in the herd. Thus, increased prevalence associated with the
thermal environment may be a combination of host susceptibility and increased exposure to the
pathogens in environment.

Thermal neutral zone and thermal stress
Sub-optimal temperature and temperature variability appear to be an important factors
associated with Salmonella infection in swine (Funk et al., 2001a; Funk and Gebreyes, 2004;
Hautekiet et al., 2008; Smith et al., 2010). One of the biological explanations is that sub-optimal
temperature might increase pig stress, which can lead to low immunity and increase
susceptibility to new infections and recrudescence of Salmonella carriers (Funk et al., 2001a;
69

Hald and Andersen, 2001; Smith et al., 2010). The mechanisms behind of increased risk of
infection when pigs are exposed to stress are complex and partially unknown (Mulder, 1995;
Berends et al., 1996). Stress is generally considered to suppress the immune system and may
lead to an increase of the occurrence of diseases (Salak-Johnson and McGlone, 2007). The term
stress is broad and not well defined; everything that disrupts the normal state of the well being of
the animal can be considered stress. Stress was initially defined as “exposure to nocuous
environmental factors (stressors) elicits a nonspecific reaction, this reaction is characterized by
enhanced pituitary-adrenal reactivity and facilitates the return to homeostasis” (Dantzer and
Mormede, 1983). Lately, stress has been defined based on the type of insult, response to the
aggression and effects on host. There are numerous environmental challenges, not only
traditional environmental stressors (e.g. heat, cold, humidity, pollutants) but also the social
environment that can cause disrupt the animal balance and consequently lead to a stress
response in animals (Salak-Johnson and McGlone, 2007). Stress responses include physiologic,
endocrinologic (hormonal), behavioral and production responses. Stress can be categorized in
groups such as social stress, transport stress, environmental stress (e.g. temperature and
humidity), and feed related stress (e.g., feed withdrawal).
Animals have a range of comfortable temperatures within which they are able to maintain
a relatively stable body temperature via behavioral and physiological means (Gaughan et al.,
2008a) called the thermal neutral zone (TNZ) (Ames, 1980; Gaughan et al., 2008a). The TNZ is
defined as a range of ambient temperatures (upper and lower critical temperatures; UCT and
LCT, respectively) at which temperature regulation is achieved simply by control of sensible
heat loss (i.e., without regulatory changes in metabolic heat production or evaporative heat loss)
(Gaughan et al., 2008a). The term thermoneutrality can have different meanings in the
70

literature: 1) the range of environmental temperatures in which heat production remains basal, 2)
range of environmental temperature over which the body temperature is normal and remains
normal while sweating and panting do not occur, 3) the range of environmental temperature that
provides a sensation of maximum comfort, and 4) the preferred thermal environment by the
animal, 5) the optimal environment in which the animal have optimum health and performance
(Ames, 1980). The different definitions are used depending on the outcome or the reason for
describing thermoneutrality.
The thermo neutral range of air temperature is dependent on age and weight in swine
(Jacobson et al.; Harmon and Hongwei, 1995) (Table 1.1). Younger pigs have a narrower TNZ
range, and they are more susceptible to cold temperatures. Older pigs have a wider TNZ, and
are less tolerant to higher temperatures. The UCT, LCT and TNZ are influenced by insulation
(animal and external insulation), breed, nutrition, exercise, production, physiological status and
health (Ames, 1980; Young et al., 1989; Gaughan et al., 2008a). Although the effective
temperature depends mainly on the ambient air temperature, other factors have an important
effect on effective temperature. Velocity of ambient air (wind and drafts), floor type, wet
surfaces, bedding and building materials, and relative humidity all have an impact on the
thermal environment of the animal (Young, 1981; Gaughan et al., 2008b). In addition, animal
behavior (e.g., grouping, huddling) affects the temperature experienced by the animal (Young,
1981). Effective ambient temperatures below the LCT result in cold stress and those above
UCT in heat stress (Ames, 1980; Gaughan et al., 2008a). Cold stress is due to the incapacity of
the animal to increase its heat production and the losses to the surrounding environment are
greater than the heat production rate (Young, 1981). Heat stress results from the animal’s
inability to dissipate sufficient heat or reduce the heat influx to maintain homeostasis of the
71

animal (homeothermy) (Gaughan et al., 2008a). From here forward, both (cold and heat stress)
are addressed as the general term thermal stress, unless otherwise mentioned.
Temperature and humidity work in conjunction to effect overall environmental conditions
on the animal. Thermal stress is caused by a combination of environmental factors including
temperature, relative humidity, solar radiation, air movement, and precipitation (Bohmanova et
al., 2007). Water vapor content of the air is an important factor because it has an impact on the
rate of evaporative loss through the skin and lungs. The amount of moisture in the air is
particularly important when the air temperature is outside of the comfort zone of the animal.
Three measurement types are used by meteorologists to quantify water vapor content: 1) wet
bulb temperature (Twb), represents the equilibrium temperature of a thermometer covered with a
cloth that has been wetted with pure water, relative humidity (RH), gives information about
saturation of the air at a given temperature, dew point temperature (Tdp) is the temperature to
which the air must be cooled for saturation to occur; and dry bulb temperature (Tdb) refers to
‘normal’ air temperature (Bohmanova et al., 2007). Thus, thermal stress has been evaluated
using both the TNZ temperature values and temperature humidity index (THI). THI combines
within the same formula both air temperature and humidity (Lucas et al., 2000; St-Pierre et al.,
2003; Bohmanova et al., 2007). There are numerous THI formulas with different weightings of
dry bulb temperature, dew or wet point temperature. The most adequate formula to express the
heat stress depends upon the species, production parameters to be measured, and climatic
conditions (Lucas et al., 2000; St-Pierre et al., 2003; Bohmanova et al., 2007). Lucas et al.,
(2000) compared two THI formulas (TH1= 0.72twb + 0.72tdb + 40.6 and THI2= 0.63twb +

72

1.17tdb + 32, where tdb and twb were the dry and wet bulb temperatures of the ambient air in C)
to determine heat stress in swine during summer. Formula 2 predicted heat stress better
especially under extreme conditions. A scale indicating the range of different combinations of
temperature and humidity was published by NWSCR (National Weather Service Central
Region, 1976) predicting the relative safety ranges of THI for livestock. The normal values for
confined livestock were considered ≤ 74; alert values were those from 75 to 78, danger values
those from 79 to 83, and emergency values were thoses ≥ 84 (Lucas et al., 2000). Temperature
humidity index > 72 caused heat stress in growing finishing pigs and consequently decreased
dry matter intake with economic losses (St-Pierre et al., 2003).
Thermal challenges (thermal stressors) range from cold to hot and are life cycle
dependent (Nienaber and Hahn, 2007). The intensity and duration of the exposure to a given
thermal stress factor will also determine animal responses (Gaughan et al., 2008a). Responses
of animals vary according to the type of thermal challenge; that is acute events result in
short term adaptive changes in behavioral, physiological and immunological responses, while
longer term challenges will impact related performance responses (e.g., altered feed intake and
heat loss which affect growth, reproduction and efficiency). When the thermal stress passes a
certain threshold disrupted behavior is observed, with impaired immunity and physiology
(Nienaber and Hahn, 2007). Acclimatization is observed through changes in physiological,
immune and adapted performance when animals are challenged with moderate thermal stressors
(Nienaber and Hahn, 2007; Gaughan et al., 2008a). The type of response and the lag time after
the thermal challenge onset varies according to the insult (intensity and duration), and to the
ability of the animal to recover (Nienaber and Hahn, 2007; Renaudeau et al., 2008). Genetics
73

might also play a role in thermal stress responses. New genetic lines of high lean growth swine
have become more susceptible to heat stress, because the total heat production is significantly
higher compared with other lines (Brown-Brandl et al., 2001).

Effects of thermal stress in swine
Physiological changes
The effects of thermal stress on swine have been extensively studied and published in the
literature. The majority of studies have been focused on heat stress. It is well established that
heat stress causes physiological, behavioral and performance changes in pigs. There is no
uniformity in terms of the threshold of the thermoneutrality, the duration of the exposure and lag
time to observe changes in animals. Some authors defined heat stress based on a single value
(such as 33 C) outside of the thermo neutral range (Collin et al., 2001). Other authors have
investigated a range of temperatures with or without a period of adaptation (Brown-Brandl et al.,
2001; Renaudeau et al., 2008), or have included the effect of humidity as well (Huynh et al.,
2005b). Thus, summarizing the effects of thermal stress based on the literature is a challenge.
The following review does not intend to be an exhaustive description of those effects but an
overview of the range of effects of heat stress observed in swine.
Animal behavior and physiological changes occur when pigs are exposed to heat stress
(Collin et al., 2001; Huynh et al., 2005b). The initial indicators of heat stress are increased
respiration rate and water to feed consumption ratio, followed by decreased heat production
and feed intake, and finally increased rectal temperature (Brown-Brandl et al., 1998; BrownBrandl et al., 2001; Huynh et al., 2005b; Renaudeau et al., 2008). The time spent lying down
74

versus eating increases with a rise of ambient temperature (Collin et al., 2001; Aarnink et al.,
2006). High temperature greatly affects the laying and excreting behavior; temperature is
inversely related to huddling and positively related with wallowing, and the total number of
bodily excretions increases with temperature (Huynh et al., 2005a; Aarnink et al., 2006). These
behavior changes of the animal are an attempt of self cooling when exposed to high
temperatures.
These behavioral changes also might increase contact with fecal material and as a
consequence, increase the risk of transmission of fecal oral diseases as Salmonella. On the
other hand, pigs exposed to cold stress show behavioral changes such as an increase in standing
and feeding times, a decrease in laying and an overall increased activity (Hicks et al, 1998).
How these differences in behavior might be related to susceptibility to infectious diseases or
transmission rates is unknown.

Production and reproductive changes
Production and reproductive performance are affected when heat stress is present for
extended periods. Growing pigs have reduced feed intake with a corresponding reduction in
growth rate after exposure to heat stress (Brown-Brandl et al., 2000; Collin et al., 2001; Huynh
et al., 2005b; Renaudeau et al., 2008). Average daily feed intake decreases about 100g/day each
each 1 C increase in ambient temperature between 24 C and 36 C, and from day 0 to 20 of
heat stress exposure. In addition, the reduction of average daily gain during the same period is
55g/ C (Renaudeau et al., 2008). Moreover, a high relative humidity combined with high

75

temperature significantly affects the average daily gain, because it limits the animal’s ability to
dissipate heat via evaporation and accentuates the effect of heat stress (Huynh et al., 2005b).
Higher fat deposition and lower protein deposition are observed in carcasses of pigs exposed to
heat stress (Brown-Brandl et al., 2000). Heat stress affects the reproductive performance of
sows and decreases milk yield and piglet growth rate (as a consequence of the reduction in milk
yield) (Black et al., 1993; Bloemhof et al., 2008).

Immune response changes
Stress, including heat and cold stress, affects the immune response in swine. Exposure to
stressors activates the hypothalamic pituitary adrenal axis, leading to a release
adrenocorticotropic hormone (ACTH) and glucocorticosteroids into the blood of stressed
animals (Dantzer and Mormede, 1983; Hicks et al., 1998). The levels of plasma corticosteroids
(e.g., cortisol) increases when pigs are exposed to stress factors such as social stress, electrical
stimulation, heat stress, and feed and water deprivation (Hicks et al., 1998). Cellular immune
response is also affected by stress. Social and heat stress affect the cellular immune response by
changing neutrophil and lymphocyte profiles, reducing cell proliferation, and reducing natural
killer cell cytotoxicity (Morrowtesch et al., 1994; Hicks et al., 1998). In addition, the response
to endotoxin is compromised in challenged piglets exposed to cold stress (Carroll et al., 2001).
Those immune responses might compromise the immunologic defense and increase
susceptibility to new infection with foodborne pathogens in farm animals (Rostagno, 2009).
In addition to the immune response, a complex network of interactions between the
central nervous system, the enteric nervous system and the gastrointestinal tract is observed in
76

stressed animals. Those interactions include: the release of neuroendocrine and stress mediators
(e.g. , glucocorticoid hormones and the catecholamines epinephrine and norepinephrine) that can
have a significant effect either on the immune system and/or the gastrointestinal tract. The
release of catecholamines during stress results in decreased gastric acid production, delayed
gastric emptying, and accelerated internal motility and colonic transit and increased stomach pH.
These changes can lead to a higher susceptibility to new infections due to greater survival and
colonization of foodborne pathogens in gastrointestinal tract (Rostagno 2009). Recent research
on microbial endocrinology has suggested a more complex interaction between the
catecholomines released in gastrointestinal tract and bacteria growth and virulence factors
(Freestone et al., 2008; Lyte et al., 2011).
In summary, stress results in a complex interaction between central nervous, immune and
gastrointestinal systems that can predispose animals to a new infections or re activate previous
infections of several foodborne diseases in livestock species. The causal pathway between stress
and foodborne pathogen infection is complex and involves central nervous system (CNS),
gastrointestinal responses and interactions between bacteria and host (Rostagno, 2009). The
interaction between the host, pathogens and environment are complex and imply host and
pathogen adaptation mechanisms. In addition, other factors such as management can alter those
mechanistic responses (Figure 1.1).

Stress and food pathogens in swine
Thermal stress may cause changes in gastro-intestinal bacterial species, genotypes and
antimicrobial resistance phenotypes in swine. The relationship between thermal stress and the
intestinal microflora of swine has been mainly reported with E. coli infections (Moro et al.,
77

1998; Moro et al., 2000; Jones et al., 2001; Mathew et al., 2003). Moro et al. (1998)
investigated the effect of cold stress on the prevalence of antimicrobial resistance in E. coli from
swine feces on a farm where no antimicrobial had been used in feed during the previous 10
years. Exposure to cold stress caused significant increases in ampicillin and tetracycline
resistance of E. coli isolates in pigs exposed to a drop in temperature of at least 15 C within 24h
before the sampling, compared to pigs within normal and stable temperatures. The cold stress
samples were collected during the winter, below the lower critical temperature (10 C) of
thermoneutrality for finishing pigs (Moro et al., 1998). In another study, pigs experimentally
infected with enterotoxigenic E. coli (ETEC) have a significant increase in fecal shedding of
ETEC when exposed to cold temperatures, compared with a non stressed group (Jones et al.,
2001). Heat stress has been associated with antibiotic resistance of E. coli as well. In an
experimental study, a significantly higher proportion of E. coli collected after heat stress (34 C
for 24h) were resistant to single or multiple antibiotics (amikacin, ampicillin, cephalothin,
neomycin and tetracycline) as compared to samples from the same pigs pre heat stress (Moro et
al., 2000). In addition, only 25% of the pre stress isolates showed multiple antimicrobial
resistance patterns (equal or greater than two antimicrobial); in contrast to 85% of the isolates of
post stress having multiple resistances. Furthermore, a significant difference was observed for
tetracycline resistance between isolates obtained from carcasses of a non stressed group (40%)
versus a stressed group (80%), suggesting that stressed animals were shedding higher numbers
of resistant bacteria that subsequently contaminated the carcasses (Moro et al., 2000). Mathew
et al. (2003) investigated the effect of cold stress on pigs fed with apramycin and the
78

antimicrobial resistance of fecal E. coli. Cold stress extended the duration of increased
antimicrobial resistance to apramycin as compared to pigs kept at thermal neutral temperatures.
Heat stress in combination with feeding apraymicin transiently increased the proportion of E.
coli resistant to apramycin as compared to pigs fed apramycin and kept at a thermal neutral
temperature (Mathew et al., 2003).
The mechanisms responsible for the increased antimicriobial resistance and bacterial
growth when pigs were exposed to thermal stress are largely unknown. The thermal stress
might result in physiological changes in the gastrointestinal tract such as motility, pH, and
fermentation acid concentrations, which may then impact the gut flora. Those environmental
changes may lead to an increased ability of bacteria to acquire resistance genes or allow the
proliferation of resistance bacteria in gut microflora (Mathew et al., 2003). In addition, enteric
bacteria respond directly to stress related neuroendocrine hormones such as catecholamines that
promote the enteric bacterial growth and alter the interactions between the intestinal mucosa and
intraluminal microorganisms. The stress can influence the bacterial infectivity in non immune
manner, due to complex interactions designated by microbial endocrinology (Freestone et al.,
2008; Rostagno, 2009; Lyte et al., 2011). The enteric growth and antimicrobial susceptibility of
enteric flora alterations in response to thermal stress is the result of complex host microbe
interactions between the pig’s physiology and the bacterial flora of the gastrointestinal tract.
Other stress factors have been associated with antimicrobial resistance and
gastrointestinal pathogens shedding in swine. Social stress, transport, and feed and water
withdrawal have each been associated with increased Salmonella and E. coli shedding in swine.
Changes in antimicrobial resistance have been reported after transport and holding stress
(Langlois et al., 1986; Molitoris et al., 1987) as well as after moving animals into and out of
79

their pens (Hedges and Linton, 1988). Weaning, mixing and handling all increased the ETEC
and generic E. coli shedding in pigs (Jones et al., 2001; Dowd et al., 2007). Callaway et al.
(2006) showed that social stress increased fecal shedding of S. Typhimurium in early weaned
piglets (Callaway et al., 2006). This is particularly important because segregated early weaned
piglets and re grouping of piglets are practices that have been widely adopted by the swine
industry; thus, the mixing of piglets might lead to social stress and consequently may increase
the susceptibility to infections and shedding of Salmonella.
Salmonella shedding is significantly increased during transport and lairage (Berends et
al., 1996; Hurd et al., 2002; Larsen et al., 2003). Transportation of pigs causes several levels of
stress; each of crowding, social status, duration of trip and feed deprivation can affect the
Salmonella shedding status during transportation. Factors such as high animal density, stress,
and feed deprivation during transport can have a strong influence on the Salmonella status of
pigs (Berends et al., 1996). Isaacson et al. (1999) demonstrated that pigs experimentally
infected with S. Typhimurium had increased shedding after transportation. In contrast, Rostagno
et al. (2005) did not find a difference in prevalence estimates when compared before and after
transportation from farm to abattoir. Factors than transportation seem to contribute to the higher
shedding observed after transport and lairage. Feed withdrawal is a common practice before
pigs are transported to slaughter, in order to decrease the risk of carcass contamination during
the evisceration (Martin-Pelaez et al., 2009). Increased pre-slaughter feed withdrawal and
lairage times lead to cecal fermentation changes, increased pH and decreased concentrations of
short chain fatty acids, and as a consequence there are increased numbers of Enterobacteriaceae
including Salmonella in market pigs (Martin-Pelaez et al., 2008; Martin-Pelaez et al., 2009). On
the other hand, a resting period on the transport vehicle decreased Salmonella shedding in pigs
80

(Rostagno et al., 2005b). In summary, several stress factors during transportation may
contribute to higher Salmonella shedding after transportation.
Stress is a broad term used to identify a range of situations which alter the animal’s
homeostasis. Stressors such as transportation, social stress, and feed withdrawal are associated
with increased Salmonella shedding in swine (Berends et al., 1996; Callaway et al., 2006;
Martin-Pelaez et al., 2009). There is a lack of studies that evaluate the association between
Salmonella shedding and sub optimal thermal environment. One possible casual pathway for
effects of the sub optimal thermal environment on Salmonella shedding is in following diagram
(Figure. 1.2). However, the causal pathway is not simple, due to the fact the stressors occur in
combination and responses to stress involve multiple interactions between nervous, endocrine
and immune systems. In order to address at least one component of this causal pathway, this
study has as an objective to investigate the association between exposure to a sub optimal
thermal environment and Salmonella shedding in finishing swine.

81

APPENDIX

82

a

Table 1.1. Upper and lower critical temperature criteria of thermal neutral zone of finishing pigs used to assess the thermal (heat and
cold) exposure.

Pig age (weeks)

Pig weight (kg)

Lag time 12h, 24h, 48h, 72h, 1 week
b

c

Lag time 1 month
b

c

LCT

UCT

LCT

UCT

10

22.8

27.8

36

18.9

26.7

21.1

27.8

47

16.7

26.7

18.9

26.7

16

58

14.4

26.7

16.7

26.7

18

70

13.3

26.7

14.4

26.7

20

85

12.2

26.7

13.3

26.7

22

98

12.2

26.7

12.2

26.7

24

c

27.8

14

b

21.1

12

a

25

109

11.1

26.7

12.2

26.7

Adapted from Harmon and Hongwei, 1995.
Lower critical temperature ( C)
Upper critical temperature ( C)

83

Figure 1.1. Responses of swine to potential environmental stressors that can have an effect on
production, immunity and animal health (Adapted from Nienaber et al. (1999)). For
interpretation of the references to color in this and all other figures, the reader is referred to the
electronic version of this dissertation.

84

Environmental Contamination
and Proliferation (feed, water,
buildings, domestic animals, wild
animals)

Sub-Optimal Thermal
Environment Thermal Stress
Management Factors (ventilation, heating,
air change buildings, cooling systems, floor)

Salmonella
Change GI bacteria
Increased shedding (E.coli, other
Enterobacteraciae, Salmonella)

Management Factors: Feed, antibiotics
stock density, transport, pigs movements

Stress
GI changes
Catecolamines
Gut motility, gastric acid
production
pH stomach, fermentation
GI motility
Pig

Increased antimicrobial
resistance
Microbial Endocrinology

Responses to stressors
(animal)
Behavior (lying and excreting)
Physiologic (RT, RR, waterfeed ratio)
Performance (ADG, ADFI)
Immunologic
(glucocorticoides
catecolamines, cellular
immune)

Figure 1.2. Potential casual pathway of the effect of suboptimal thermal environment on Salmonella shedding in swine.
85

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CHAPTER 2
Longitudinal study of Salmonella shedding in naturally infected finishing pigs

Manuscript based on these data was published on Epidemiology and Infection (Pires, A.F.A.,
Funk, J.A., Bolin, C. A. 2012. Longitudinal study of Salmonella shedding in naturally infected
finishing pigs. Epidemiol.Infect. (Published online on November13th 2012))

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ABSTRACT
A 3 year longitudinal study was conducted on a multi-site farrow to finish production
system. For each of 18 cohorts at three finishing sites, 50 pigs were randomly selected. Fecal
samples were collected every 2 weeks for 16 weeks. Salmonella was cultured from 453 (6.6%)
of 6836 fecal samples. The pig level incidence of Salmonella was 20.8% (187/899 pigs).
Salmonella prevalence varied between both sites and cohorts within sites. The proportion of
positive samples decreased over the finishing period from 12.9% to 2.8%. Intermittent detection
of Salmonella was found in more than 50% of pigs that were positive at more than one
collection. The finding that the majority of pigs shed intermittently has implications for
surveillance and research study design when determining Salmonella status. The variability in
shedding over time, as well as between and within sites, cohorts and pigs suggest that there may
be time variant risk factors for Salmonella shedding in swine.

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INTRODUCTION
It has been well documented that Salmonella species are one of the major causes of
foodborne diseases in the US and worldwide (Greig and Ravel, 2009; CDC, 2011; Scallan et al.,
2011). In the US alone, it is estimated that 1,027 million nontyphoidal Salmonella human
infections result in 19,336 hospitalizations and 378 deaths annually (Scallan et al., 2011), costing
$ 365 billion in direct medical expenditures per year (CDC, 2011). Swine are a potential
reservoir for human salmonellosis. The most common serotypes isolated in swine (S.
Typhimurium, S. Heidelberg, S. Agona, and S. Infantis) are common to those found in human
cases (Foley et al., 2008; CDC, 2010). It has been suggested that reduction of Salmonella
contamination of pork requires interventions at three levels: pre harvest (farm), harvest
(slaughter) and post harvest (distribution systems and consumer handling) (Lo Fo Wong et al.,
2002; Boyen et al., 2008). In order to put in place on farm control and intervention measures it
is crucial to understand Salmonella infection dynamics in swine.
A large number of epidemiological studies have been conducted to determine prevalence
and risk factors for Salmonella infection in swine. Most of these studies have used a
cross sectional study design. A limited number have assessed the fecal prevalence over time,
with longitudinal studies showing high variability in Salmonella shedding at the farm, cohort and
individual animal level (Funk et al., 2001; Beloeil et al., 2003; Kranker et al., 2003; Nollet et al.,
2005; Rajic et al., 2005; Farzan et al., 2008; Dorr et al., 2009; Rostagno et al., 2012).
Longitudinal studies at the pig level during the finishing phase have reported time variability of
fecal shedding associated with cohort (or batch) of pigs (Funk et al., 2001; Beloeil et al., 2003;
Kranker et al., 2003; Nollet et al., 2005). Intermittent fecal shedding is also commonly reported
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in epidemiological studies of swine (Beloeil et al., 2003; Kranker et al., 2003; Nollet et al.,
2005).
Therefore, longitudinal studies at the individual level based on bacteriological culture
should be performed in order to investigate the dynamics of Salmonella infection in swine. The
objective of this study was to describe the shedding pattern of Salmonella in feces of naturally
infected finishing pigs.

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MATERIALS AND METHODS
A longitudinal study was conducted on a multi-site farrow to finish production system
located in the Midwestern United States. The presence of Salmonella in the system had been
confirmed by culture of pooled fecal samples prior to initiation of the study. Selection criteria
for the production system were willingness to cooperate in a long term research project and to
share health management and production records. The production system had three site
management, meaning that overall production was separated into three stages of production,
breeding and farrowing, nursery (weaning until approximately 10 weeks of age) and finishing
(10 weeks to slaughter, 24 to 26 weeks), with each stage housed at separate sites. The
production system had all in all out management in nursery and finishing sites. This system
consisted of 2 farrowing sites (F1 and F2), 2 nursery sites (N1and N2), and 12 finishing sites.
The farrowing sites had a total inventory of 3700 sows (F1=1300, F2=2400), the average
one time inventory of the finishing sites was 25000 (75000 finishing pigs/year marketed).
During the study period the system transitioned from 2 farrowing sites to one farrowing site of
3000 sows. The number of nursery and finisher sites remained unchanged. Three finishing sites
(A, B, and C) were conveniently selected, based on building design and willingness to participate
in the study. At each finishing site one barn was selected for study inclusion. Site A had four
barns in separate buildings. Pigs were allocated into 40 pens (20 25 pigs per pen). Sites B and
C had identical building structures. Each site had four barns grouped in two buildings (two
barns/rooms with one shared wall). Each barn housed approximately 1000 pigs. Pigs were
housed in 12 pens; ten pens were initially stocked with pigs at placement (eight pens with a
range of 100 125 pigs and two pens with a range of 40 50 pigs). The remaining two pens were
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used for sick pens or pigs deemed to be at risk for illness. Sites A and B were finishing sites
(10 26 weeks of age). Site C transitioned to a weaning to finishing site after the second cohort
of pigs. For the wean to finish cohorts at site C, piglets were placed in the barn at 3 weeks of age
and remained until marketing. Finishing site A received pigs from nursery N1 in all cohorts; site
B received pigs from nursery N2 in 4 cohorts and from N1 and N2 in the last 2 cohorts of pigs.
The first cohort for site C was supplied from nursery N2, for all other cohorts piglets were placed
directly from the farrowing sites due to the transition to a weaning to finishing site.

Sample collection
Nursery sampling
In order to evaluate the Salmonella status of the cohort of pigs prior to sampling during
the finishing phase, ten pools were collected from the nursery rooms approximately one week
prior to movement to the finishing barn. A pool consisted of a minimum of five g of fresh fecal
material collected from five different locations on the same pen floor (25g/pool). In the wean to
finishing site (site C), ten pools were collected from ten random pens when pigs were
approximately 9 weeks of age.

Environmental sampling
Finisher barns were cleaned and disinfected between batches of pigs. The disinfectants
(Synergize, Preserve International, Reno, NV, USA and VirkonS, Antec International, Suffolk,
UK) were alternated following the standard operating procedure of the production system. In
order to assess contamination, culture of environmental samples was performed after cleaning
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and disinfection and before placement of pigs in the barn. Drag swabs and environmental swab
samples were obtained from cleaned and disinfected floors, walls, gates and feeders/drinkers
following previously described methods (Kingston, 1981). Briefly, swabs were moistened with
ten ml of sterile buffered peptone water (BPW, Acumedia, Neogen Corporation, Lansing,
MI,USA) before the collection. To sample floors, one drag swab was used for four pens in site
A. In sites B and C, one drag swab was used per one to two pens depending on pen size. To
sample other environmental surfaces, a single 4X4 gauze moistened with BPW was used to
sample each surface. Ten, 5, 3 and 2 swabs were collected from floors, walls, gates and
feeders/waterers, respectively in each barn prior to every cohort.

Individual fecal sampling
At the beginning of each cohort, 50 pigs (10 ± 2 weeks old) were randomly selected and
individually identified with ear tags. Random number generation was conducted in Microsoft
Excel 2007 (Microsoft, Redmond, WA, USA). In site A, a simple random sample was generated
to select one pig per every pen (n=40). Another ten pens were randomly selected to identify and
select a second pig using a simple random sample (additional ten pigs for a total of n=50/barn;
30 pens with one study pig, ten pens with two study pigs). A random proportional sampling
scheme based on the number of pigs in each pen was conducted in sites B and C within each
cohort. A range of one to seven pigs per pen was selected. In sites B and C no pigs were
selected for study inclusion from the pens identified for sick or at-risk pigs.
Individual fecal samples were collected from the rectum with gloved hand, and placed in
sterile containers (Specimen cups, VWR International LLC, PA, USA). Gloves for collecting
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the feces were changed between pigs. After collection, samples were stored at ambient
temperature for transport to the laboratory. Individual pig fecal samples were collected every 2
weeks for 16 weeks (eight total sampling periods per pig). A total of 400 individual samples (50
pigs X 8 sample periods) per cohort and 7200 fecal samples overall (400 samples X 18 cohorts)
were planned for collection.

Bacteriological culture
Fecal samples
Bacteriological culture for Salmonella was performed by the Diagnostic Center for
Population and Animal Health, Michigan State University. Fecal samples were transported to the
o

laboratory the day of collection, or stored for 48 hours at 2.8 C.
Fecal samples were cultured using standard enrichment methods from Davies et al.
(2000). Briefly: for pooled samples from the nursery, 25 g of the pooled fecal samples were
diluted in 225 mL of Tetrathionate broth (TTB) (Becton Dickinson, Sparks, MD, USA) and
incubated at 37 C for 48 hours. For individual pig fecal samples 10 g of the individual fecal
samples were inoculated into 90 mL of TTB and incubated at 37 C for 48 hours. After
incubation, an aliquot (100 L) of the fecal TTB solution was inoculated into 9.9 mL of
Rappaport-Vassiliadis broth (RV, Becton Dickinson, Sparks, MD, USA) and incubated at 42 C
for 24 hours. The RV broth was plated onto Xylose Lysine Tergoitol 4 agar (XLT4, Remel,
Thermo Fisher Scientific, Lenexa, KS, USA) selective agar plates and incubated at 37 C,

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overnight. Suspect Salmonella colonies from microbiological culture were screened using
Salmonella poly O antisera antiglutination (Becton Dickinson, Sparks, MD, USA).
Environmental samples
Environmental samples were cultured following the same protocol (Davies et al., 2000)
using a volume of TTB sufficient to submerge the swabs (~60ml).

Data analysis
Bacteriological culture data were entered into an Excel 2007 spreadsheet using
appropriate coding and subsequently verified for accuracy by checking each entry with the
original hard copy result. The spreadsheets were transferred to a relational database (Access
2007, Microsoft Corporation, Redmond, WA, USA). Data was retrieved from the database and
imported into SAS 9.3 (SAS Institute, Cary, NC, USA) for data management and statistical
analysis.
Descriptive statistics of demographic data (number of pigs sampled, gender), loss to
follow-up and morbidity were presented in proportions.
Descriptive statistics of bacteriological culture were generated for the nursery, barn
environment, site, cohort, pig, and fecal sample (observation). Salmonella apparent prevalence
(proportion of positive samples/tested) and respective 95% confidence intervals were estimated
at each unit of observation: cohort (e.g., all collections combined within cohort), site (e.g., all
cohorts combined), pig age (by collection period) and individual sample. Pearson Chi-squared
analysis with Bonferroni adjustment was used to compare apparent prevalence among sites. Chisquared test for trend in proportions (Cochran-Armitage Test) was applied to test change of
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apparent prevalence over time. Correlations between the proportion of positive fecal samples in
a cohort (e.g., all collections combined within cohort) and respective proportion of Salmonella
positive samples in nursery and environment were determined using Spearman’s rho. A
significance level of 0.05 was used for all comparisons.
Patterns and duration of shedding were estimated for those pigs which met the following
inclusion criteria: 1) survival until marketing (excluded n=3 dead and n=5 early shipment); 2) no
more than one period from which a sample was not collected (excluded n=1); 3) had no more
than two negative cultures between two positive culture results (excluded n=10).
In order to estimate the duration of shedding of individual pigs, we assumed that the
shedding began 7 days prior the first detected positive culture and lasted until 7 days after the
last isolation. The 7 days interval was selected taking into account data from experimental
studies indicating that pigs start to shed Salmonella as early as 2 to 7 days post exposure
(Fedorka-Cray et al., 1994) and as late as 7 to 14 days (van Winsen et al., 2001; Osterberg et al.,
2010) after exposure to a Salmonella contaminated environment or when commingled with pigs
shedding Salmonella. This interval (7 days) was also the midpoint between two consecutive
sampling periods.

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RESULTS
Demographic results
A total of 900 pigs were selected for inclusion in the study. Forty six per cent were
barrows or castrated males (410/900) and 54% were females (490/900). The total loss to followup for fecal sample collection was 5.1% (364/7200). Causes for loss to follow-up were: death,
unable to collect a specimen (e.g., empty rectum, sick animal), or shipped to market prior to final
collection. A total of 17 pigs died during the study (17/900; 1.9%). The majority of the pigs
were sampled 8 (71.4%; 643/900) or 7 times (23.1%; 208/900).
At the observation level (individual pig times number of sample periods pig was
observed), diarrhea was described in 2.4% (164/6836) of the observations. At the pig level,
15.1% (136/900) were observed to have diarrhea at least once.

Nursery and barn environment
The total proportion of positive samples and respective 95% C.I. of nursery, barn
environment samples and sites, stratified by cohort are summarized in Table 2.1. Pooled fecal
samples from the source nursery were collected and cultured in 17/18 cohorts. Salmonella was
detected in at least one nursery pool in 76.5% (13/17) of the cohorts. A total of 36.5% (62/170)
of the pooled nursery samples were Salmonella positive. The proportion of positive nursery
samples ranged from 0% to 100% among cohorts (Table 2.1).
Environmental samples were collected for all cohorts. Salmonella was detected in at
least one environmental swab in 61.1% (11/18) of the cohorts. The total number of positive

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swabs was 40 (40/360; 11.1%). The proportion of positive barn environment swabs ranged from
0% to 85% among cohorts (Table 2.1).

Site, cohort and age apparent prevalence
Salmonella was isolated from at least one sample type (nursery, environmental or
individual fecal) sample at all three sites. In 17/18 cohorts at least one individual fecal sample
was positive. Salmonella was cultured from 6.6 % (453/6836; 95% C.I. 6.0 7.2%) of individual
fecal samples. The proportion of positive fecal samples within a cohort (eight collection periods
combined per cohort) ranged from 0% to 44.1%. Within site, the proportion of positive fecal
samples per cohort (six cohorts/site) ranged from 1.5% (6/396; 95% C.I. 0.6-3.3%) to 12.0%
(46/382; 95% C.I. 9.0 15.7%) in site A, 0.6% (2/362; 95% C.I. 0.1 2.0%) to 44.1% (156/354;
95% C.I. 38.8 49.4%) in site B and 0% to 6.1% (24/393; 95% C.I. 4.0 9.0%) in site C (Table
2.1).
For 17 cohorts with both nursery and environmental swab collections, there were 9
cohorts with at least one positive sample in both samples types that also had at least one
individual fecal sample positive. Three cohorts were Salmonella positive in the nursery but
Salmonella negative for environmental swabs. One cohort was negative in the nursery and had
at least one environmental swab positive. Three cohorts were negative for both sample types.
One cohort had at least one positive sample for both nursery and environmental samples but was
negative for individual fecal samples. The proportion of Salmonella positive samples was
significantly greater in those cohorts in which both the nursery and the barn environment were
Salmonella positive (p value<0.05) (Table 2.2). No significant difference was found among
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cohorts negative for both types of samples and nursery positive and environment negative nor
nursery negative and environment positive (p value>0.05) (Table 2.2). There was a positive
association between the proportion of positive samples in a cohort and the proportion of positive
pooled nursery samples (rho=0.76, p value=0.0002). There was also a positive association
between the proportion of positive barn environmental swabs (rho=0.59, p value=0.01) and the
proportion of positive fecal samples in a cohort.
There was a significant difference between sites in the overall proportion of positive
samples (p value <0.0001). Site B (11.2%; 247/2203; 95% C.I. 9.9 12.6%) had a higher
prevalence than site A (6.3%; 147/2338; 95% C.I. 5.3 7.4%) and site C (2.6%; 59/2295; 95%
C.I. 2.0 3.3%). Site A also had a greater proportion of positive samples than Site C (p<0.0001).
For all 18 cohorts, the proportions of positive samples per cohort were plotted by age
(Figure. 2.1). The overall median was 2.0%; 25%, 75% and 95% quartiles were 0%; 7.4%;
25.5%. The overall proportion of positive samples decreased significantly over the collection
periods (p value <0.0001). The Salmonella apparent prevalence decreased from 12.9%
(115/890; 95% C.I. 10.8 15.3%) at the beginning of the finishing period (10 weeks old) to 2.8%
(20/706; 95% C.I. 1.7 4.3%) at the end of finishing phase (24 26 weeks old) (p value <0.0001).
Variation in prevalence was seen between sites, between cohorts within site and within
cohorts. For each site, the within-cohort Salmonella apparent prevalence was plotted by pig age
(Figure. 2.2.a; Figure. 2.2.b; Figuere. 2.2.c). Within-site and across cohorts and age, the
apparent prevalence ranged from 0 to 24.5% (95% C.I. 13.34 38.9%) in site A, from 0% to
71.4% (95% C.I. 56.7 83.4%) in site B, and from 0 to 20.4% (95% C.I. 10.2 34.4%) in site C.

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Pig apparent prevalence and duration of shedding
Most pigs were detected as Salmonella positive for the first time at the first collection
period (10 weeks of age, 61.5%; 115/187). This was followed by collections 2 and 3 (12 and 14
weeks of age; 14.4%, 27/187), collection 4 (16 weeks of age; 4.3%, 8/187), collection 6 (20
weeks of age 3.7%; 7/187) and collections 5, 7 and 8 (18, 22 and 24 weeks of age (0.5%; 1/187).
Overall incidence of Salmonella was 20.8% (187 /899 pigs; 95% C.I. 18.2% 23.6%). Of the
positive pigs, 87 were culture positive once (46.5%) and 27 (14.4%), 31 (16.6%), 17 (9.1%), 10
(5.4%), 6 (3.2%), and 7 pigs (3.7%) were positive, 2, 3, 4, 5, 6, and 7 times respectively. Only
two pigs were Salmonella positive in all eight collection periods. The duration of shedding was
clustered within site and cohort. The majority of the pigs with two or more positive samples
belonged to Site B (61/100 pigs), with sites A and C having 30 and 9 pigs detected as culture
positive for Salmonella at two or more collection periods, respectively. In site B, two cohorts
had the majority of pigs (53/61) with two or more positive samplings (40 and 13 pigs, in cohorts
2 and 4, respectively). In site A, two cohorts had the majority of pigs with two or more positive
samples (19/30; 9 and 10 pigs in cohort 2 and 4, respectively).
There were 95 pigs detected positive in more than two sampling occasions that had
consecutive sampling collections. Of these, 46.3% (44/95) had consecutive positive culture
samplings, 23.2% (22/95) had one culture negative fecal sample between positive culture
samples and 30.5% (29/95) were culture negative in two or more occasions between the first and
last culture positive sample collection period for each pig.
A total of 168 pigs met the inclusion criteria for estimation of shedding period. The
median time of shedding was 14 days (std = 32.5; range 14 112 days). Eighty five pigs (50.6%)
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shed 14 days or less; 15 pigs (8.9%) shed for 28 days or less, 18 pigs (10.7%) shed for 42 days or
less, 11 pigs (6.6%; 11/168) shed for 56 days or less and 39 pigs (23.2%; 39/168) shed between
70 to 112 days.

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DISCUSSION
Estimates of Salmonella prevalence in finishing pigs in the US range from 3.4% to 48%
(Davies et al., 1997; Funk et al., 2001; Hurd et al., 2004; Bahnson et al., 2006; Gebreyes et al.,
2006; Dorr et al., 2009; USDA-APHIS, 2009; Wang et al., 2010; Rostagno et al., 2012). The
observed proportion of Salmonella positive samples and cohort prevalence were within the range
of these reports. The overall incidence of positive pigs was 20.8%, which is, to the best of our
knowledge, the first estimate of incidence in naturally infected swine in one large system in the
US.
Several longitudinal studies have been conducted at the farm (van der Wolf et al., 2001;
Rajic et al., 2005; Rajic et al., 2007; Farzan et al., 2008; Rostagno et al., 2012) and cohort/pig
group level (Merialdi et al., 2008; Dorr et al., 2009; Vigo et al., 2009). A limited number of
studies have repeatedly sampled individual pigs (Funk et al., 2001; Beloeil et al., 2003; Kranker
et al., 2003; Nollet et al., 2005). Similar to these previous studies, we report variability of
prevalence by site, cohort and within pig. This may suggest there are risk factors at the site,
cohort and pig level that might be associated with Salmonella prevalence. This variability
reinforces that point estimates of prevalence might misclassify farm and pig status and that
prospective studies are needed to assess time dependent risk factors for Salmonella in swine
with consideration for risk factors that may be distributed at different levels of organization
(farm, cohort, pig) (Dohoo et al., 2010).
The majority of the pigs were detected as Salmonella positive at the beginning of the
finishing period (10 weeks old). Although individual sampling during the nursery phase was
not performed in this study, Salmonella was isolated in nursery pool samples from a majority of
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the cohorts and there was a positive association between the nursery pool prevalence and the
proportion of positive individual samples within a cohort. This suggests that pigs were exposed
to Salmonella in the nursery and may have been shedding at arrival to the finishing barn.
Salmonella shedding during the nursery phase has been reported (Nollet et al., 2005), in some
cases representing the peak prevalence during the nursery period (Kranker et al., 2003). Several
authors have reported increased prevalence when pigs were moved to finishing units (Nollet et
al., 2005; Vigo et al., 2009), which may be a result of multiple potential factors: stress caused by
transportation, comingling with new pigs, changes in feed type and exposure to residual
contamination (Nollet et al., 2005; Vigo et al., 2009).
Contaminated facilities are a source of Salmonella (Funk et al., 2001; Mannion et al.,
2007; Dorr et al., 2009; Zewde et al., 2009) and may in part explain the high prevalence of
Salmonella at the first collection period. In agreement with other authors we observed that
cleaning and disinfection did not eliminate Salmonella in the barn environment. The elimination
of Salmonella from barn environments is difficult and residual contamination might be
responsible for new infections (Funk et al., 2001; Beloeil et al., 2003; Mannion et al., 2007;
Zewde et al., 2009). The positive association between the proportion of positive barn
environment swabs and the proportion of positive individual samples within a cohort suggests
that the contaminated environment may have contributed to Salmonella infections in the
finishing phase.
Overall, prevalence decreased as pig age increased. Other authors have reported a
decrease in prevalence during the finishing period (Kranker et al., 2003; Nollet et al., 2005; Vigo
et al., 2009). It is unclear whether this association represents the natural history of Salmonella in
swine, with young animals being more susceptible and ultimately clearing the infection over
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time, or if other factors are involved. Further research to understand whether control of
Salmonella in young pigs ultimately would decrease the risk of shedding at the time of harvest is
warranted.
More than 50% of the Salmonella positive pigs were detected two times or more. Other
studies that have followed pigs over time have reported a lower percentage of pigs that were
detected more than two times. Beloeil et al. (2003) reported that a majority of
pigs shed only once in weekly samplings. In other studies the comparison is not as direct, since
in this study the sampling period was more frequent than other reports (Funk et al., 2001;
Kranker et al., 2003). In this study, pigs identified as Salmonella positive more than two times
were clustered within site and cohort. This is in agreement with Kranker et al. (2003), who
reported characteristic patterns (shorter or longer periods of shedding) by cohort. This might
suggest that there are cohort level effects that are related to duration of shedding or transmission
dynamics.
The median and range of shedding duration in this study is similar to that described by
Kranker et al. (2003), who reported a mean duration of shedding of 18 or 26 days, range of 7 to
101 days. Although our estimates are limited by an imperfect diagnostic test, the sampling
frequency and the assumption of no new infections, these data present critical information
regarding the duration of shedding in naturally infected swine. Further research to understand
risk factors for duration of Salmonella shedding in swine are warranted.
There was intermittent detection of shedding in more than fifty percent of the pigs with
multiple culture positive collections. Salmonella carriers can shed intermittently and for long
periods (Funk et al., 2001; Kranker et al., 2003; Scherer et al., 2008). It is difficult to separate
intermittent shedding of Salmonella from intermittent detection or new infections. Despite being
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an imperfect diagnostic test, fecal culture is considered the ‘gold standard’ for Salmonella
isolation. Estimates of the relative sensitivity of fecal culture range from 6.5% to 95%,
depending on culture method and parallel estimation of the sensitivity (Davies et al., 2000; Funk
et al., 2000; Funk, 2003; Hurd et al., 2004; Rostagno et al., 2005; Love and Rostagno, 2008).
Although a relative short sampling interval (two weeks) was conducted in this study, new
infections could occur between sampling occasions. Therefore, the intermittent shedding could
be either intermittent detection of an on going infection or a new infection after clearance of a
previous infection.
These data represent one production company in one region of the United States.
Although this may limit external validity, we believe that this limitation is minimal. This farm is
typical of many US swine production systems in size and production practices. Furthermore,
there are many similarities between the results in this study compared to others both in the US
and other countries. A further limitation for interpretation is in regards to the univariate analyses
reported in this paper. Statistical inferences should be interpreted carefully, as the analyses did
not take into account the clustered nature of the data (samples within pigs, pigs within pens, pens
within barns, barns within sites), which may bias the results reported. Further analyses using
multivariate analysis accounting for the clustered data structure is presented in chapter 4. The
bias presented by the univariate analyses would tend to result in an increased risk of Type I error
(Clarke, 2008). Despite this limitation, the findings presented in this paper are consistent with
what has been previously reported in the literature (Funk et al., 2001; Beloeil et al., 2003;
Kranker et al., 2003; Nollet et al., 2005; Mannion et al., 2007; Vigo et al., 2009; Zewde et al.,
2009).

130

These descriptive data regarding the incidence, duration and pattern of shedding in swine
provide critical data for understanding risk factors for Salmonella in finishing swine. The
variability and clustering of Salmonella shedding by site, cohort and pig not only suggest a need
to evaluate time variant risk factors, but also guide the design of future epidemiological studies
for identification of potential risk factors at different levels of clustering (site, cohort and pig).
Future research of the epidemiology of Salmonella in swine should focus on longitudinal study
designs focused on multilevel and time variant risk factors. This study also reinforces that
estimates of point prevalence might misclassify herd or pig Salmonella status.

131

ACKNOWLEDGMENTS
This work was supported by USDA-NRI, Epidemiologic Approaches to Food Safety
Grant 2007 01775. The authors thank the participating pork producers and their staff for
collaborating in the investigation, and staff and students at Michigan State University for their
technical support.

132

APPENDIX

133

Table 2.1. Proportion of samples positive for Salmonella spp by site and cohort (samples represent individual fecal samples, pooled
fecal samples from the source nursery and barn environmental swabs) and respective 95% confidence intervals.
a

Environment
Site
/Cohort
Site A
1
2
3
4
5
6
Site B
1
2
3
4
5
6
Site C
1
2
3
4
5
6
NS: not sampled

Nursery

b

Cohort
Total positive fecal
samples/ tested
%

%

95% C.I.

%

95% C.I.

5.0
25.0
0.0
85.0
5.0
5.0

0.1-24.9
8.7-49.1
NA
62.1-96.8
0.1-24.9
0.1-24.9

60.0
80.0
NS
60.0
20.0
10.0

26.2-87.9
44.4-97.5
NS
26.2-87.8
2.5-55.6
2.5-44.5

30/388
42/396
7/390
46/382
16/386
6/396

7.7
10.6
1.8
12.0
4.1
1.5

5.3-10.9
7.8-14.1
0.1-3.7
9.0-15.7
2.4-6.6
0.6-3.3

10.0
10.0
0.0
30.0
5.0
0.0

1.2-31.7
1.2-31.7
NA
11.9-54.3
0.1-24.9
NA

10.0
100.0
20.0
100.0
0.0
0.0

0.25-44.5
69.2-100
2.5-55.6
69.2-100
NA
NA

12/383
156/354
4/379
57/339
2/362
16/386

3.1
44.1
1.1
16.8
0.6
4.1

1.6-5.4
38.8-49.4
0.3-2.7
13.0-21.2
0.1-2.0
2.4-6.6

1/387
24/393
6/390
18/371
0/376
10/378

0.3
6.1
1.5
4.9
0.0
2.6

0.01-1.4
4.0-9.0
0.6-3.3
2.9-7.6
NA
1.3-4.8

0.0
0.0
0.0
15.0
5.0
0.0

NA: not applicable

NA
0.0
NA
NA
80.0
44.4-97.5
NA
0.0
NA
3.2-37.9
60.0
26.2-87.8
0.1-24.9
10.0
2.5-55.6
NA
10.0
2.5-44.5
a
total of 20 environmental samples per cohort
b

total of 10 pooled samples per cohort
134

95% C.I.

Table 2.2. Distribution of cohorts and proportion of samples positive for Salmonella spp by the Salmonella status of nursery and
a
environmental swabs .

Nursery and environment status
Nursery + environment +
Nursery + environment Nursery - environment +
Nursery- environment a

Number of
positive
cohorts

Number of
negative
cohorts

Number of
positive fecal
samples/tested

9
3
1
3

1
0
0
0

383/3771
38/1150
2/362
23/1163

Proportion of
positive fecal
b
samples (%)
10.2A
3.3BD
0.6CE
2DE

17 cohorts are represented, 1 cohort was excluded as no nursery samples were collected

b

different letters indicate a significant difference (p value < 0.05) of proportion of positive fecal samples

135

95%
C.I.
9.2-11.2
2.4-4.5
0-2.0
1.3-3.0

Figure 2.1. Box plot representing the distribution of Salmonella positive fecal samples within
each cohort by pig age.

136

Figure 2.2.a. Apparent prevalence (individual fecal samples) for each cohort (C1 C6) by pig
age in site A. Error bars represent 95% exact confidence intervals for proportions.

137

Figure 2.2.b. Apparent prevalence (individual fecal samples) for each cohort (C1 C6) by pig
age in site B. Error bars represent 95% exact confidence intervals for proportions.

138

Figure 2.2.c. Apparent prevalence (individual fecal samples) for each cohort (C1 C6) by pig age
in site C. Error bars represent 95% exact confidence intervals for proportions.

139

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140

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144

CHAPTER 3
Direct quantitative real time PCR for enumeration of Salmonella in feces of naturally
infected pigs

145

ABSTRACT
Quantification of Salmonella in asymptomatic pigs can be used to identify control
measures and to assess the risk of carcass contamination during slaughter. The objectives of this
study were: 1) to compare direct quantitative real-time PCR (q PCR) detection of Salmonella to
microbiological culture and 2) to quantify the fecal concentration of Salmonella in naturally
infected pigs. Individual fecal samples (positive (n=443), negative (n=1225) determined by
microbiological culture) were submitted to q PCR. A receiver operating characteristic curve
was used to identify the quantification cycle (Cq) cut off that optimized sensitivity and
specificity. A Cq cut off of 37.52 cycles optimized clinical sensitivity (15.4%) and specificity
(99.6%). At this cut off, direct q PCR categorized 99.6% (1220/1225) of culture negative
samples as negative. For culture positive samples, 15.4% (68/443) were detected by q PCR, but
3

only 3.4% (15/443) were within the q PCR quantifiable range (≥ 10 CFU/g of feces). Of these
3

6

latter samples, the concentration range was 1.06x10 1.73x10 CFU/g feces. Of the samples
with high Salmonella concentrations 7 were collected from one pig and 3 samples were collected
from its pen mates. Direct q PCR may be an alternative to traditional culture dependent
methods for detection of pigs with high fecal concentrations of Salmonella, but not for detection
of pigs shedding low concentrations of Salmonella, which represented the majority of pigs in this
study. When high shedding was detected it was clustered within a single pig and its pen mates.
These data can contribute to quantitative risk assessments of the association between
concentrations of Salmonella shed by pigs during the finishing phase and the risk of carcass
contamination at slaughter.
146

INTRODUCTION
Salmonella species are one of the major causes of foodborne diseases in the United States
and worldwide (Greig and Ravel, 2009; Henao et al., 2011; Scallan et al., 2011). Salmonella is
still one the most important bacteriological zoonotic hazards transmissible from pork to
consumers (Fosse et al., 2009). A significant number of human cases of salmonellosis (1% to
25%) have been related to consumption of pork and pork products (Berends et al., 1998; Hald et
al., 2006; Ravel et al., 2009; EFSA, 2010b; Guo et al., 2011). Swine are asymptomatic
reservoirs for Salmonella and shed intermittently in their feces (Wood and Rose, 1992; Scherer
et al., 2008), which can be a source of carcass contamination (Baptista et al., 2010; van Hoek et
al., 2012) and subsequent transmission to humans (Bollaerts et al., 2009).
Since Salmonella is a ubiquitous organism, eradication as a control measure is not viable.
Decreasing the concentration of Salmonella shed by swine may represent a more achievable
disease control target. Enumeration of bacterial load can be used to identify contamination
pressure and to identify effective control measures to reduce contamination in swine herds
(Fravalo et al., 2003). In addition, data are needed for quantitative microbial risk assessments
and for modeling transmission patterns of Salmonella (Bollaerts et al., 2009; Lanzas et al., 2011).
Most of what is known about the concentration of Salmonella shed in pig feces is based on
experimental studies (Wood and Rose, 1992; Gray et al., 1996; Osterberg and Wallgren, 2008;
Scherer et al., 2008; Osterberg et al., 2009; Rostagno et al., 2011). A limited number of studies
have quantified Salmonella concentration in feces of naturally infected swine. These were either

147

cross sectional studies (Fravalo et al., 2003; Fablet et al., 2006; van Hoek et al., 2012) or
estimates of pen contamination in lairage (O'Connor et al., 2006; Boughton et al., 2007).
Traditionally, quantification of Salmonella in fecal samples has been based on
culture dependent methodologies. Those methodologies include use of enrichment and selective
culture media, such as most probable number (MPN) technique; direct plating; use of modified
semi solid Rappaport Vassiliadis (MRSV) medium; or use of the mini MRSV MPN technique
(Fravalo et al., 2003; Fablet et al., 2006; O'Connor et al., 2006; Boughton et al., 2007; Osterberg
and Wallgren, 2008; Osterberg et al., 2009). Quantitative methods based on culture are time
consuming (3 7 days), labor intensive and costly; therefore, it can be impeditive to use in studies
with a large number of samples.
Culture independent methods such as quantitative real time polymerase chain reaction
(q PCR) assays may be more efficient for high through put diagnostic testing and may be more
representative of the true bacterial concentration in the matrix tested. Q PCR has been used to
quantify Salmonella in food matrices, pig feces and on pig carcasses (Malorny et al., 2008; Park
et al., 2008; Abley, 2011; Krämer et al., 2011; Löfström et al., 2011). Some of those methods
included pre enrichment of the sample before the DNA extraction (Malorny et al., 2008; Krämer
et al., 2011), although the use of pre enrichment media before the DNA extraction may
misrepresent the “true” bacterial count. Direct quantification of Salmonella has been applied in
food matrices (Fukushima et al., 2007; Cheng et al., 2009; Elizaquivel et al., 2011), pork carcass
swabs (Guy et al., 2006; Löfström et al., 2011), chicken rinses (Wolffs et al., 2006) and fecal
material (Harris et al., 2007; Pusterla et al., 2010; Abley, 2011). The quantification limit varies,
depending on matrix, assay and processing. Few studies have enumerated Salmonella in swine
148

fecal samples using real time PCR without enrichment (Harris et al., 2007; Abley, 2011).
Quantitative real time PCR is an alternative to the traditional quantitative culture-dependent
method to directly quantify the bacterial concentration in swine feces. It allows enumeration of
Salmonella in a large number and variety of samples in an efficient time-cost and automated way
(Malorny et al., 2008; Elizaquivel et al., 2011; Löfström et al., 2011). The objectives of this
study were: 1) to compare direct q PCR detection of Salmonella in swine feces to the
microbiological culture and 2) to quantify the fecal concentration of Salmonella in naturally
infected pigs.

149

MATERIALS AND METHODS
The samples were collected from a longitudinal study on a multi site farrow to finish
production system located in the Midwestern, United States. The primary aim of this study was
to describe Salmonella shedding of naturally infected finishing pigs (Pires et al., 2012). Criteria
used for the selection of production system, finishing sites, pig selection, sample collection and
laboratory isolation of Salmonella have been described in detail elsewhere (Pires et al., 2012).
Briefly, three sites from a multi site farrow to finish production system were selected for the
study. At each 4 barn site, 1 barn was selected for the study inclusion. For each site selected, 6
consecutive cohorts of pigs were included in the study. Each 1000 head inventory barn housed
pigs from 10 weeks of age until market (24 25 weeks of age). At the beginning of each cohort,
50 pigs (10 ± 2 weeks old) were randomly selected and individually identified. Individual fecal
samples (10 g) were collected from pigs every 2 weeks for 16 weeks (8 total sample periods per
cohort). Fecal samples were cultured using standard methods described elsewhere (Pires et al.,
2012). Briefly, fecal samples (10 g) were inoculated into 90 mL of Tetrathionate broth (TTB)
(Becton Dickinson, Sparks, MD) and incubated at 37 C for 48 hours. After incubation, an
aliquot (100 L) of the fecal TTB solution was inoculated in into 9.9 mL of
Rappaport Vassiliadis broth (RV) (Becton Dickinson, Sparks, MD) and incubated at 42 C for
24 hours. The RV broth was plated onto Xylose Lysine Tergitol 4 agar (XLT4) (Thermo Fisher
Scientific, Lenexa, KS) selective agar plates and incubated at 37 C, overnight. Suspect
Salmonella colonies from the XLT4 were screened using triple sugar iron (Becton Dickinson,
Sparks, MD) and urea agar slants (Becton Dickinson, Sparks, MD). Salmonella suspect
150

colonies then were screened using Salmonella poly O antisera antiglutination (Becton Dickinson,
Sparks, MD).
An aliquot of each fecal sample (200 mg) was stored at 80 C. A random selection of
culture negative samples (n= 1225) and all culture positive samples (n=443) were submitted for
q PCR. A list of culture negative samples was generated by simple random sampling of
negative samples from 17 out of 18 cohorts, using a commercial statistical software package
(Proc surveyselect procedure; SAS 9.3; SAS Institute, Cary, NC). The sample size of culture
negative samples was based on a Bayesian approach for sample size calculations for surveys to
substantiate freedom from an infectious agent (Johnson et al., 2004) and using software available
online (Bayesfreecalc2; http://www.epi.ucdavis.edu/diagnostictests/module02.html).
Assumptions for calculation of sample size of culture negative samples for q PCR evaluation
were: less than 5% of the culture negative samples would be false positive by direct q PCR,
based on expert opinion; microbiological culture sensitivity greater than 0.6 and mode of 0.7;
microbiological culture specificity greater than 0.95 and a mode of 0.99. Based on these
assumptions, a sample size of 1274 provided at least 93% confidence that the true prevalence
was zero.
The DNA was extracted from feces using the Qiagen Qiamp Stool Mini Kit (Qiagen,
Valencia, CA) according to manufacturer instructions. The PCR primers and probe targeting the
invA gene were as described previously (Hoorfar et al., 2000). The q PCR reaction conditions
were modified as needed to accommodate use of reagents and equipment different from those
described previously (Hoorfar et al., 2000). The cut off for q PCR assay was set at 45 cycles.
The limit of detection of the q PCR was determined using sterilized fecal samples spiked with
151

serial 10 fold dilutions of Salmonella isolated from a pig in the current study. The DNA
extracted from the spiked fecal samples was used for generation of calibration curves. The lower
quantification limit of the PCR was calculated to be 8.3 copies of target per reaction, which is
equivalent to 917 CFU/g of feces. The linear dynamic range for quantification was determined to
2

6

be 9.17 x10 to 9.17 x10 CFU/g of feces. The q PCR was performed in triplicate for each and a
calibration curve was generated for each plate of samples.
The Cq value from the real time q PCR was used as a proxy measure of fecal bacterial
load. The Cq value is inversely proportional to the amount of bacteria load in fecal sample, the
lower the Cq value the higher the fecal concentration. The Cq values were recorded for all tested
samples, a Cq average was obtained for those samples detected by the q PCR assay (Cq average
was calculated when 2 or 3 wells were detected positive). All other samples that were tested and
not detected by q-PCR were assigned a single value (Cq = 45) for the purpose of the comparative
study.
Descriptive statistics (median, mean standard error, 95% confidence intervals) of Cq
values were described for those samples detected by q PCR based on the analytical cut off. In
order to optimize the best compromise between the diagnostic sensitivity and specificity, the Cq
was compared to the ‘gold standard’ (fecal culture) by means of receiver operating characteristic
curve (ROC) (Greiner et al., 2000). A pig was Salmonella positive if the fecal sample tested
culture positive at each sample period. Statistical analyses were performed using a commercial
statistical software package (MedCalc for Windows, version 12.1.3.0, MedCalc Software,
Mariakerke, Belgium). For each q PCR result (Cq value), sensitivity, specificity, positive and
negative likelihood ratios and respective 95% confidence intervals (exact binomial estimation)
152

were estimated, relative to the fecal culture, and the ROC curve was constructed by plotting the
sensitivity versus 1 specificity. The area under the curve (AUC), standard error and 95%
confidence intervals (binomial exact estimation) were estimated based on non parametric
methodology (DeLong et al., 1988). TheYounden index of diagnostic accuracy (sensitivity +
specificity 1) (Greiner et al., 2000) was calculated for the Cq cut off which optimizes the
sensitivity and specificity. This cut-off was defined as the diagnostic Cq cut off. Individual
fecal samples were re categorized in negative versus positive based on diagnostic Cq cut off in
order to estimate the Salmonella fecal concentration.
Descriptive statistics of concentrations were presented as copy numbers of invA gene/g of
feces as well as with a scoring system. Individual fecal samples were classified into 4 scores
based on q PCR and culture results. The four scores were: 0) culture-negative and
q PCR negative; 1) culture positive and q PCR negative; 2) culture positive and q PCR
positive and culture-negative and q PCR positive, not within quantifiable range; 3)
culture positive and q PCR positive within the quantifiable range. The concentration gradient
was assumed to increase from score 0 to score 3.

153

RESULTS
Cq values were generated for 69 culture positive samples (15.6%, 69/443), with a median
Cq of 35.5 (95% C.I. 35.4 35.7, range = 24.6 to 38.6) and 25 culture negative samples (2.0%;
25/1225) with a median Cq of 38.4 (95% C.I. 38.1 39.4, range = 34.7 to 42.8). For the purpose
of ROC analysis, a Cq value of 45 was attributed to 374 culture positive and 1200 culture
negative samples.
The receiver operating characteristic curve (ROC) curve for the q PCR test is shown in
Figure 3.1. The area under the curve (AUC) was 0.569 (95% C.I. 0.545 0.593), which can be
interpreted that a randomly selected Salmonella culture positive sample has a lower Cq value
than a randomly selected Salmonella culture negative sample 56.9% of the time. The AUC was
significantly different from a non informative curve (p value<0.0001). The direct fecal q PCR
test was of low accuracy (0.5<AUC<0.7) as compared to the gold standard of fecal culture
(Greiner et al., 2000; Gardner and Greiner, 2006). The optimal diagnostic cut off of Cq value
was 37.52, which maximized clinical sensitivity (15.4%; 95% C.I. 12.1 19.0%), specificity
(99.6%; 95% C.I. 99.1 99.9%) and corresponds to the maximum Youden index (0.149). The
positive likelihood ratio was 37.6 (95% C.I. 30.2 46.8%) and the negative likelihood ratio was
0.85 (95% C.I. 0.4 2.0%). The likelihood ratio of a positive test represents the link between the
odds of the pretest and post test probability of disease, given a positive test result (Greiner and
Gardner, 2000; Gardner and Greiner, 2006). In this case, a q PCR positive is 37.6 times more
likely in a culture positive fecal sample than in a culture negative fecal sample.

154

Based on the optimal diagnostic cut off (Cq = 37.52) and the maximum Younden index,
99.6% (1220/1225) of the Salmonella culture negative samples were q PCR negative. Of the
culture positive samples, 15.4% (68/443) were detected by q PCR, but only 3.4% (15/443) of
3

the samples were detected in triplicate and within the quantifiable range (≥ 10 copies of the
invA gene).
The distribution of samples in Salmonella concentration score categories, were classified
as follows: Score 0) (negative on both tests) 1220; Score 1) (positive culture /negative q PCR)
375; Score 2 (positive culture /positive q PCR and negative culture/positive q PCR) 58; Score
3) (positive culture/positive q PCR, within quantifiable range) 15 (Figure 3.2).
3

6

For the 15 samples in score 3 the concentration ranged from 1.06x10 to 1.73x10 copies
5

6

of invA gene/g feces (median = 2.97x10 and std = 6.16x10 ). These 15 samples were collected
from a total of 9 pigs (Figure 3.3). Forty seven percent (7/15) of the fecal samples in the score 3
group were collected from the same pig (Pig ID 4, Figure 3.3). This pig also had the highest
5

6

concentration of Salmonella shed (range 4.08x10 to 1.73x10 copies of the invA gene/g feces),
which occurred at the collection period immediately prior to marketing. Forty-four percent (4/9)
of the pigs in the score 3 group were not only from the same cohort, but also the same pen.

155

DISCUSSION
There are significant knowledge gaps regarding quantitative risk for Salmonella shedding
on farm and risk of carcass contamination. It is intuitive that the concentration of bacteria shed
in feces is related to both transmission dynamics on farm, as well as to the risk for carcass
contamination, however, to the date data are limited. This is likely due to many factors, not the
least of which is the challenge of quantifying Salmonella concentrations in complex matrices
such as feces. This study evaluates the potential application of direct q PCR to identify pigs
3

shedding high concentrations (>10 CFU/g) of Salmonella in their feces. This approach removes
the impediments of both logistics for labor and the challenges of interpretation of concentration
after enrichment. The detection limit of this PCR assay without enrichment is in agreement with
3

4

other reports of 10 to 10 gene copies per gram of feces (Malorny and Hoorfar, 2005; Harris et
al., 2007; Malorny et al., 2008; Abley, 2011). One of the studies evaluated the performance of
the real time PCR assay in fecal samples inoculated with Salmonella enteritidis (ATCC 13076)
1

8

with final concentrations ranging from 10 to 10 CFU/mL. There was a strong positive
correlation between the sample concentrations and q PCR results, but the q PCR concentration
estimates were 10 fold lower than the inoculated concentration (Abley, 2011). Despite this
limitation, a practical application of this methodology may be to detect and determine fecal load
in swine shedding high concentrations of Salmonella.
In a recent study, a method was developed to quantify Salmonella, combining a short
non-selective enrichment (8h) followed by q PCR (Krämer et al., 2011). This allowed
enumeration of low numbers (1.4 CFU/10g) in cork borer samples (skin) from pig carcasses by
156

harvesting the cells in log phase of bacterial growth (Krämer et al., 2011). However, this
methodology was applied to a specific type of sample and sample processing method (DNA
extraction, pre enrichment, etc); therefore, its applicability to swine fecal samples must be
further tested (Krämer et al., 2011). A further consideration is the lack of knowledge regarding
whether it is critical to food safety outcomes to be able to quantify Salmonella concentrations in
3

animal feces at less than 10 CFU/g. Further research to understand the association between
fecal concentration and carcass contamination can elucidate what analytical sensitivity is
required.
One potential explanation for the low sensitivity of the q PCR is that the targeted gene
(invA gene) might not be present in all the Salmonella strains present on the farm and as a result,
positive culture samples might not be detected by q PCR. The assay used in the current study
has been shown to detect 110 Salmonella strains (Hoorfar et al., 2000), among those are the most
common serovars found in swine (e.g., S. Typhimuirium, S. Heidelberg, S. Agona , S. Derby). It
is known that some Salmonella strains (S. Senftenberg and S. Litchfield) have natural deletions
within the Salmonella pathogenecity island 1 involving the inv, spa, and hil loci (Ginocchio et
al., 1997). We did not determine the serovars of the Salmonella isolates in this study. Future
research to classify the serovars isolated in this study is planned.
The quantitative limit of real time PCR without enrichment should be taken into account
in studies that use this methodology for enumeration of Salmonella in swine fecal material.
Quantitative real time PCR might be a good alternative to the traditional quantitative
culture dependent methods, because it allows enumeration of Salmonella in a large number and
variety of samples in an efficient time cost and automated way (Malorny et al., 2008; Löfström
157

et al., 2011). One of the potential applications of this methodology is for identification of high
shedders (>103 CFU/gram), either at the farm or in lairage. This might be of particular interest to
identify high shedders in order to apply control measures, such as segregation of pigs during
transportation and during harvest.
The majority of the pigs in this study shed low concentrations, below the quantitative
limit of q PCR. These results are in agreement with those using other diagnostic tests for
quantification. A study using the mini-MRSV MPN technique, reported that 86% of fecal
samples from naturally infected pigs sampled at the abattoir had less than 200 organisms /g
(Fravalo et al., 2003). Using the same technique, estimated concentrations of 2.4 to 350
organisms per gram of feces were reported in pooled fecal samples of finishing pigs on French
farms (Fablet et al., 2006). Quantitative studies in lairage environments, using enrichment media
and MPN technique have reported variable and relatively low bacterial concentrations; median
2

pen surface concentrations ranged between 1.8 11.5 organisms/100 cm (Boughton et al., 2007)
and 457 1071 organisms/ml of slurry collected from lairage pens (O'Connor et al., 2006). More
recently, a study in a Dutch slaughterhouse reported an estimated mean concentration of 1.88
1.42 log10 MPN/g on rectal swabs of carcasses sampled after exsanguination (van Hoek et al.,
2012). In experimental studies, carrier pigs shed intermittently in concentrations below the
detection limit (< 10CFU/gram) for two months after being infected (Scherer et al., 2008).
To the best of our knowledge this is first study to quantify the fecal concentration of
Salmonella in repeated sampling of individual, naturally infected finishing pigs. The few fecal
samples with high concentrations of Salmonella in the feces were clustered within pig and pen.
Further investigation of whether there are potential risk factors for shedding high concentrations
158

of Salmonella, and the transmission dynamics of Salmonella in groups with and without “high
shedders” is an area worth investigating. Quantitative risk assessment studies have suggested
that most exposures of swine to Salmonella are at doses below the infectious dose (EFSA,
3

2010a). Doses greater than 10 CFU increase the probability of infection in swine (Osterberg
and Wallgren, 2008; EFSA, 2010a). The infectious dose of Salmonella is dependent upon
serovar, exposure to contaminated fecal material (mass fecal material/fecal mass ingested) and
duration of exposure (van Winsen et al., 2001; Jensen et al., 2006; Osterberg et al., 2010). There
are likely interactions between risk of infection with both concentration shed as well as the
number of animals shedding. These data may provide insight into comparison of intervention
strategies targeted at control of pigs that shed high concentrations, for perhaps long periods of
time, as compared to interventions more generally targeted at control of prevalence at the group
level.
The importance of high shedders for risk of contamination at the slaughterhouse is
unknown. For example, is the greater public health risk associated with having a large
population of pigs shedding very low concentrations of Salmonella, or a small proportion of pigs
shedding high concentrations? Current efforts for surveillance at slaughter focus on prevalence
outcomes (Baptista et al., 2010; USDA-FSIS, 2010). Quantitative risk assessment studies have
reported that interventions to reduce Salmonella cases in humans due to pork related products
includes reducing slaughter pig prevalence by reducing the number of infected pigs with high
infection/contamination loads entering the slaughterhouse (EFSA, 2010a). Identification of high
shedders may be more effective for preventing carcass contamination. The main factor
determining risk of human illness reported in these studies was gross contamination (i.e. large
159

numbers of CFUs per carcass), where such contamination is usually via fecal leakage from a
heavily infected pig, then cross contamination to a substantial number of carcasses further
down the processing line (EFSA, 2010a). Understanding the relationship between the
concentration of Salmonella shed and public health risk is an area of critical concern for food
safety. A combination of further risk analyses as well as economic analyses of the cost of
identifying high shedders relative to overall prevalence is needed to develop appropriate
surveillance and intervention strategies.

160

ACKNOWLEDGMENTS
The authors thank the participating pork producers and their staff for collaborating in the
investigation, and staff and students at Michigan State University for their technical support.
This work was supported by USDA NRI, Epidemiologic Approaches to Food Safety Grant
2007 01775.

161

APPENDIX

162

Figure 3.1. Receiver operating characteristic curve (ROC) for the real time PCR to detect
Salmonella in 1668 pig fecal samples (AUC = 0.569, Sensitivity = 15.4% Specificity = 99.6%).
Solid line AUC, dashed lines 95% CI, light line AUC = 0.50).

163

1400

1220

1200
1000
800
600
375

400
200

53

15

Score 2

Score 3

5

0
Score 0

Score 1

Culture negative

Culture positive

Figure 3.2. Distribution of 1668 pig fecal samples classified in scores, based on culture and
direct quantitative real time PCR (score 0: culture negative and q PCR negative; score1:
culture-positive and q PCR negative; score 2: culture-positive and q PCR positive and
culture negative and q PCR negative, not within quantifiable range; score 3: culture–
3
positiveand q PCR positive within quantifiable range, >10 CFU/g).

164

invA gene/ g feces

2.0E+06
1.8E+06
1.6E+06
1.4E+06
1.2E+06
1.0E+06
8.0E+05
6.0E+05
4.0E+05
2.0E+05
1.0E+03

1
2
3
4
5
6
7
8
9

1

3

5

7

9

Pig ID
Figure 3.3. Concentration of Salmonella invA genes in fecal samples of 9 pigs, belonging to
score 3 (culture positive and quantitative real time PCR positive in the quantifiable range,
3
>10 CFU/g).

165

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166

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171

CHAPTER 4
Multilevel analysis to evaluate the association between environmental thermal parameters
and Salmonella shedding in finishing pigs

172

ABSTRACT
The objectives of this study were to evaluate the association between the thermal
environment in the barn and Salmonella status in finishing pigs and to estimate the proportion of
the model variance attributable to cohort, pig and individual sample level effects. For these
purposes, individual fecal samples from 900 finishing pigs (8 collections per pig) were
repeatedly collected from 18 cohorts (50 pigs per cohort) on 3 sites of a multi site
farrow to finish production system in a longitudinal study. Pen temperature and humidity were
measured every 2 minutes during the study period. The thermal parameters of interest were:
hourly average, minimum and maximum lagged temperatures, hourly temperature variation,
temperature humidity index (THI) and cumulative number of hours/degree above and below the
thermal of neutral zone at the pen level prior to fecal sampling for 6 time periods (12h, 24h, 48h,
72h, 1 week and 1 month). Additional potential risk factors at the individual (e.g., gender, health
events), cohort (e.g., mortality, morbidity, Salmonella nursery status) and pen level were also
evaluated. Multilevel logistic models using generalized linear models, with random intercepts at
pig, pen and cohort levels to account for clustering (individual samples nested within pigs, pigs
nested within pens, pens within cohorts) were constructed. The outcome variable was
Salmonella fecal status of the individual sample. Cold exposure (temperatures below the thermal
neutral zone) and exposure to a THI >72 were both associated with risk Salmonella shedding.
Nursery Salmonella status, site, pig age and cohort mortality rate were also associated with
Salmonella shedding.
The largest proportion of model variance was associated with the individual fecal sample
(44.7%) followed by cohort (24.1%) and pen (20.7%). The present study allowed investigating
173

the association of time variant thermal factors and Salmonella shedding. Interventions that
target the thermal environment may have an effect on reducing Salmonella shedding in swine
and also improve pig well being and production efficiency. Alternatively, thermal parameters
may be used to identify groups of pigs at high risk for Salmonella shedding. Future studies
should be performed to investigate the cost efficacy of interventions to improve the thermal
environment to decrease Salmonella in swine.

174

INTRODUCTION
Salmonellosis remains a major foodborne disease threat to public health worldwide
(Greig and Ravel, 2009; CDC, 2011; Scallan et al., 2011). A seasonal pattern of human
salmonellosis is well described, with the highest incidence in summer (Naumova et al., 2007).
Seasonal variation of foodborne diseases has been related with oscillations of several
environmental factors (e.g., temperature, humidity and precipitation) (Naumova et al., 2007).
Among those environmental factors, ambient temperature has been consistently associated with
human salmonellosis worldwide (Bentham and Langford, 2001; D'Souza et al., 2004; Kovats et
al., 2004; Fleury et al., 2006; Naumova et al., 2007; Castronovo et al., 2009). In general human
cases increased 1 to 6 weeks after peak ambient temperature (Bentham and Langford, 2001;
D'Souza et al., 2004; Fleury et al., 2006; Naumova et al., 2007; Lake et al., 2009). While
short term lag times between high ambient temperature and illness may suggest that cross
contamination and bacterial multiplication on food occur close to the point of consumption
during food preparation (Bentham and Langford, 2001; Lake et al., 2009), long term lag times
suggest that ambient temperature affects Salmonella risk earlier in the food chain, including at
the farm, the slaughterhouse, distribution systems or in the home (Bentham and Langford, 2001;
D'Souza et al., 2004; Fleury et al., 2006; Lake et al., 2009). Overall, the association between the
ambient temperature several weeks prior to the onset of the human cases suggests that
temperature might affect Salmonella dynamics at the farm level. Those effects might be caused
either by creating an environment favorable for the proliferation of bacteria in the environment
and consequently increasing bacterial pressure and exposure to livestock, or by increasing the
animal susceptibility to new or recurring infections.
175

A number of studies have investigated the seasonality of Salmonella infection in swine
with mixed results. On one hand, some studies reported no seasonality (Benschop et al., 2008;
Baptista et al., 2010) while others reported higher prevalence during different seasons; either
with higher seroprevalence in winter and fall (Carstensen and Christensen, 1998; Christensen
and Rudemo, 1998; Hald and Andersen, 2001; Smith et al., 2010) or summer (Hautekiet et al.,
2008) and higher fecal prevalence in winter and spring (Funk et al., 2001a).
Season may serve as a proxy for many potential risk factors. Seasonality may represent
other changes in management practices during those periods which could increase Salmonella
risk or potentially a relationship between changes in environmental factors (e.g., temperature,
humidity) and Salmonella shedding. Environmental factors such as temperature, rainfall and
sunshine have been associated with Salmonella prevalence in swine. Finishing pigs exposed to
wide variations in daily high temperature were at greater risk of high Salmonella prevalence
(Funk et al., 2001a). In addition, large differences in long term averages of the monthly mean
temperature, as well as high rainfall and hours of sunshine were associated with higher
Salmonella seroprevalence in UK pigs (Smith et al., 2010). In both studies the environmental
parameters were retrieved from the closest weather station. Therefore, the recorded
environmental parameters might not reflect the environment in closed barns. Moreover, herds
that had ventilation control settings above the upper critical values (> 26 C, for pigs of 90 kg)
had a higher seroprevalence compared with herds with controlled programmed temperature
within the thermal neutral zone (TNZ) (Hautekiet et al., 2008). A limitation of all of these
studies is that they focused on investigation of risk factors at the herd level and were
cross sectional study designs. There is a lack of knowledge of risk factors at the pig level and
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of time dependent factors; esprcially namely, environmental thermal parameters within the barn
and the association with Salmonella dynamics.
The objectives of this study were to evaluate the association between environmental
thermal parameters in the barn and Salmonella shedding in finishing pigs, and to estimate the
proportion of total model variance attributable to cohort, pig and individual sample level effects.
We hypothesize that there is an association between sub optimal thermal parameters in the barn
and Salmonella shedding in finishing pigs.

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MATERIALS AND METHODS
Study design
A longitudinal study was conducted on a multi site farrow to finish production system
located in the Midwestern United States. The presence of Salmonella had been confirmed by
culture of pooled fecal samples prior to initiation of the study. Selection criteria for the
production system were willingness to cooperate in a long term research project and to share
health management and production records. The production system had three site management,
meaning that overall production was separated into three stages of production: breeding and
farrowing, nursery (weaning until approximately 10 weeks of age) and finishing (10 weeks of
age until slaughter, 24 to 26 weeks), with each stage housed at separate sites. The production
system had all in all out (AIAO) management in nursery and finishing sites. Three finishing
sites (A, B, and C) were conveniently selected, based on building design and willingness to
participate in the study. At each finishing site one barn was selected for study inclusion. Site A
had four barns in separate buildings, with cold weather mechanical ventilation and natural
ventilation for warm weather ventilation. Pigs were allocated into 40 pens (20 25 pigs per pen).
Dry feeders were shared in every two pens (20 feeders per barn) and each pen had two nipple
waterers. Sites B and C had identical building structures: each site had four barns grouped in
two buildings (two barns / building with one shared wall) and total mechanical ventilation
(tunnel ventilation capable for warm weather). Each barn housed approximately 1000 pigs. Pigs
were housed in 12 pens; ten pens were initially stocked with pigs at placement (eight pens with a
range of 100 125 pigs and two pens with a range of 40 50 pigs). The remaining two pens were
used for sick pens or pigs deemed to be at risk for illness. Double tube feeders (wet/dry) were
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located in 8 larger pens and single tube feeders (wet/dry) in 4 smaller pens. Pens were separated
by open, metallic gates and a central alley divided the barn. All buildings (A, B and C) had total
slatted concrete floors and a deep pit that was emptied at least once a year. Propane heaters were
used for heating at all sites. Sites A and B were finishing sites (10 26 weeks of age). Site C
transitioned to a weaning to finishing site after the second cohort of pigs. For the wean to finish
cohorts at Site C, piglets were placed in the barn at 3 weeks of age and remained until marketing.
Site A cohorts were sampled from June 2008 to August 2010; site B cohorts from July 2008 to
September 2010; site C cohorts from June 2009 to August 2011.

Sample size
The sample size employed in this study was selected to detect an expected difference of
6% in the prevalence between the exposed group (12%) and non-exposed group (6%) to ambient
temperature greater than 23.9 C (based on preliminary data of pigs in the 18 to 22 week old
range). Assumptions were a fixed type 1 error of 5% (two-tailed test), 80% confidence and
adding 20% due to loss of power associated with the inclusion of confounders and loss to followup. Considering intra-class correlations of 0.75 and 0.90 due to repeated sampling within
individual pigs (8 sampling periods), a total of 853 pigs and 996 pigs were estimated,
respectively (Twisk, 2007). As a compromise between the two estimates a total of 900 pigs were
sampled in 18 cohorts (50 pigs per cohort, 8 samples per pig, 7200 total individual pig fecal
samples).

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Sampling of individual fecal samples
At the beginning of each cohort, 50 pigs (10 ± 2 weeks old) were randomly selected and
individually identified with ear tags. Random number generation was conducted in Microsoft
Excel 2007 (Microsoft, Redmond, WA, USA). In site A, a simple random sample was generated
to select 1 pig per every pen (n = 40). Another 10 pens were randomly selected to identify and
select a second pig using a simple random sample (additional 10 pigs for a total of n = 50 / barn;
30 pens with 1 study pig, 10 pens with 2 study pigs). A random proportional sampling scheme
based on the number of pigs in each pen was conducted in sites B and C within each cohort. A
range of 1 to 7 pigs per pen was selected. In sites B and C no pigs were selected for study
inclusion from the pens identified for sick or at risk pigs.
Individual fecal samples were collected from the rectum with gloved hand, and placed in
sterile containers (Specimen cups, VWR International LLC, PA, USA). Gloves for collecting
the feces were changed between pigs. After collection, samples were stored at ambient
temperature for transport to the laboratory. Individual pig fecal samples were collected every 2
weeks for 16 weeks (eight sampling periods per pig).

Laboratory protocol for isolation of Salmonella
Bacteriological culture for Salmonella was performed by the Diagnostic Center for
Population and Animal Health (DCPAH), Michigan State University. Fecal samples were
transported to the laboratory the day of collection or stored for 48 hours at 2.8 C. Fecal
samples were cultured using standard methods described previously (Pires et al., 2012).
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In order to evaluate the Salmonella status of the cohort of pigs prior to sampling during the
finishing phase, 10 pooled fecal samples were collected from the nursery rooms approximately
one week prior to movement to the finishing barn in sites B and C. In the wean to finishing site
(Site C), 10 pools were collected from 10 random pens (1 pool/pen) when pigs were
approximately 9 weeks of age. A pool consisted of a minimum of 5 g of fresh fecal material
collected from 5 different locations on the same pen floor (25 g/pool). Contamination of the
study barns was assessed by culture of barn environmental samples after cleaning and
disinfection and before placement of each cohort of pigs in the barn. Drag swabs and
environmental swab samples were obtained from cleaned and disinfected floors, walls, gates and
feeders/drinkers (total of 20 samples per cohort) following previously described methods (Pires
et al., 2012). Nursery and environmental samples were cultured using methods described
previously (Pires et al., 2012).

Environmental data collection and description of barn ventilation systems
The thermal environment of the barns was monitored using a real time system for the
continuous measurement of temperature and humidity. Wireless network temperature sensors
were used to monitor the temperature (Darr and Zhao, 2008) and commercially available weather
resistant temperature and humidity data loggers (Hobo U23 Temperature / Relative Humidity,
Onset HOBO Data Loggers, Bourne, MA, USA) were distributed to obtain humidity data and
served as an alternative back up system for temperature data.

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The Site A barn used natural ventilation during summer, with temperature controlled by
means of automatic curtains located on the north and south side walls of the building. In colder
weather, the curtain was fully closed and temperature was controlled by exhausting air via three
12 inch ventilation fans. Two fans were located on the west end of the building while one was
located near the east end wall. The barn used 4 ceiling mounted mixing fans to promote equal
temperature distribution. The barn had 10 box inlets equally distributed in the roof of the
building for negative pressure drawing of air from the barn attic during cold weather. An
automatic controller operated the curtains and fans with the set points manually adjusted at least
monthly or as deemed necessary by the farm personnel. Twenty wireless temperature sensors
were installed in the barns with one sensor placed every 2 pens (10 sensors per side). Ten
weather resistant temperature and humidity loggers were distributed every four pens.
The barns in site B and C barn were fully mechanical tunnel ventilated buildings. The
barns operated with 5 stages of ventilation. The minimum stage utilized a 36 inch variable fan
while the remaining four fans were 48 inch variable fans. The barn had one curtain walls, one
curtain opened to the outside environment, while the second curtained wall was shared with the
adjoining barn. While the curtain associated with the outside lowered based on ventilation
needs, the shared curtain wall remained closed at all times. The barns had six box inlets equally
distributed along in the ceiling for negative pressure ventilation during winter. Twenty two
wireless temperature sensors were installed inside the barn, for each pen three were 3 sensors, 2
on gates of adjacent pens and 1 placed in the center near the feeders. Peripheral sensors were
placed between pens, contributing data for the closest two pens. Ten temperature and humidity

182

data loggers (Hobo U23 Temperature/Relative Humidity, Onset HOBO Data Loggers, Bourne,
MA, USA) were installed near the feeders.

Environmental thermal parameters
Temperature and humidity were recorded every 2 minutes twenty four hours/day for the
entire placement period of each cohort. Data was manually downloaded from the barns every
two weeks. An Excel macro (Excel 2007, Microsoft, Redmond, WA, USA) was used to sort
temperature and humidity data and calculate the environmental parameters at each sensor and
pen for the measurement period. The pen averages of sites B and C were estimated using 3
sensors, and 1 sensor monitored every two pens in site A. The environmental parameters defined
as exposures of the interest were: 1) absolute temperature at the sampling time; 2) hourly
average temperature; 3) hourly variation (variance of average temperature); 4) maximum
lagged temperature; 5) minimum lagged temperature; 6) the cumulative degrees and hours
below the lower critical temperature of the TNZ; 7) the cumulative degrees and hours above the
upper critical temperature of TNZ ; 8) temperature humidity index (THI); 9) the cumulative
degree and hours above the THI threshold (72) for finishing pigs (St-Pierre et al., 2003). The
upper and lower critical temperature of TNZ ( C) criteria for lag times 12h to 1 week and 1
month are presented in Table 4.1 and were based on pig age (Harmon and Hongwei, 1995). The
temperature humidity index used in this study was THI= 0.63twb + 1.17tdb +32, where tdb and
twb are the dry and wet bulb temperatures of the ambient air in C (Lucas et al., 2000). For all
parameters except absolute temperature, hourly and cumulative calculations for each parameter
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were calculated for every pen for 6 time periods prior to the time of fecal sampling (12h, 24h,
48h, 72h, 1week and 1 month).

Description of other variables
The following data were recorded at each respective unit of observation (e.g., pig, pen
and cohort). At the pig level, gender, age (in weeks), diarrhea or any other symptom of illness,
movement to a sick or subject pen, were recorded by the project personnel at each collection
time. A pig was considered to have an abnormal health status if one of the following events
occurred at the sampling time: 1) diarrhea; 2) sick or being moved to the sick pen; 3) undersized
pig; 4) subject pig (defined by farm personnel: a pig that appears abnormal for any reason and is
tagged with a unique tag and housed separately); 5) any sign of disease observed by research
team personnel (e.g. lameness, diarrhea, respiratory signs). Pens were categorized as ‘sick’,’
subject’ and ‘normal pen’ in addition to the environmental parameters which were measured at
the pen level. At the cohort level, the mortality, morbidity (total number of treatments as a
proxy for illness), total number of subject pigs, type of treatment (e.g., antimicrobial,
anti inflammatory therapy), Salmonella status of the nursery and barn environment, and season
of each collection were recorded. Season was defined as follows: spring (March to May),
summer (June to August), fall (September to November) and winter (December to February).

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Software used for data base management and statistical analyses
Data management: exclusions and validation of data
An electronic database was created using Microsoft Access (Access 2007, Microsoft
Corporation, Redmond, WA, USA) to record all the laboratory, environmental and field data
(pig, cohort, farm). Data were imported into SAS 9.3 (SAS Institute, Cary, NC, USA) for data
management and statistical analysis (descriptive statistics and model building). All the statistical
analyses were performed in SAS 9.3 unless stated otherwise.
Bacteriological culture data were entered into a spreadsheet (Excel 2007) using
appropriate coding and subsequently verified for accuracy by checking each entry with the
original hard copy results. The spreadsheets were transferred to the relational database. A
subset of individual pig observations data (n = 860) was verified using a random selection of the
records for each pig variable. The sample size for data verification was based on an estimated
20% record entry error + 5% error with 95% of confidence interval, using an internet based
calculator (available online at http://epitools.ausvet.com.au/content.php?page=1Proportion).
The environmental parameter data were transferred to the database. Prior to statistical analysis
of environmental parameters, data were explored by means of descriptive statistics and graphical
visualization and evaluated for unlikely values. Outliers and extreme values outside of
biologically plausible ranges were replaced as missing values before performing the statistical
analysis.

185

Model building
Associations between the Salmonella status of finishing pigs and the risk factors at the
cohort , pig and pen level were evaluated. In order to account for the clustering of the data in
a four level hierarchical structure, multilevel models were applied, since individual fecal
samples were nested within pigs, pigs within pens, and pens within cohorts. A multilevel logistic
model with random intercepts at the pig , pen and cohort levels was fitted using PROC
GLIMMIX using a residual pseudo likelihood subject specific expansion method (RSPL) with
optimization technique of Newton –Raphson with ridging. The dependent variable was at the
individual fecal sample Salmonella status (yes/no). Site (A, B, C) was considered as a fixed
effect. The final models were fitted with random intercepts and a random slope on age (time) at
the pig level in order to account for auto correlation of sampling within pig (trend model)
(Masaoud and Stryhn, 2010; Snijders and Bosker, 2012b).
Correlations between the independent variables were assessed based on Pearson’s and
Spearman’s coefficients depending on whether the normality condition was met or not. If the
value of the correlation statistic between two independent variables was equal to or greater that
0.8 at a p

0.05, different approaches were conducted as follows. Independent models were

built for each environmental variable (i.e., hourly average and maximum lagged temperature) for
each time period (12 h, 24 h, 48 h, 72 h, 1 week and 1 month). Environmental variables
measured in the same lag time were tested in same model when pair wise correlation was less
than 0.8% (maximum lagged and minimum temperatures; average hourly temperature and
variation).

186

Before model building, the linearity assumption between the log odds of outcome and
continuous predictors was tested using the following approaches: testing the quadratic term in
the model, categorizing the predictor to see if the coefficients increased uniformly and/or plotting
the continuous predictor against the logit of the outcome using the lowess curve (STATA version
11 StataCorp, College Station, TX, USA). If the linearity assumption was not met, a quadratic
term was added based on visualization of a curvilinear shape of the lowess curve, or the variable
was transformed using adequate transformation (natural log) or categorized, depending on the
variable (Dohoo et al., 2010d).
Initially, a total of 61 environmental variables were examined and analyzed using
descriptive statistics and graphics. The independent variables tested included thermal parameters
measured at the pen level (environmental parameters, type of pen), pig – level factors (gender,
health status and age), cohort – level (mortality, morbidity, nursery and barn Salmonella status,
season) and site. Independent variables were screened in univariable analysis using a 25%
significance level (p value < 0.25). All environmental variables of interest were included in
multivariable models even if not significant in univariable screening. Manual building was
conducted in multivariable models by backward elimination and independent models were
constructed for the environmental parameters at different lag times and for highly correlated
thermal variables (r >= 0.8). First-order interactions with biological plausibility and main effects
were tested using Wald’s test (p value < 0.05). The variable site (A, B and C) was forced into
the models. Interactions and main effects were removed one at a time. In the final model
potential confounders based on causal diagrams (pig health status, morbidity, and season) were
evaluated. A variable was considered a confounder if it caused a change greater than 20% to the
coefficient of a statistically significant variable when the potential confounder was removed from
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the model (Dohoo et al., 2010a). Hosmer-Lemeshow goodness-of-fit test was assessed on final
models (Dohoo et al., 2010b).

Variance components
The proportion of model variance at each hierarchical level was estimated using a method
based on latent response variables (or logistic threshold method) (Dohoo et al., 2010c; Snijders
and Bosker, 2012a). The latent variable technique allows estimation of variance components and
intra-class correlation by fixing error variance at

2

/3 and a mean of zero at the individual fecal

sample level (Dohoo et al., 2010c). A random effect model with intercept as the only fixed term
(null model), and final models (with one of the main exposures), and with the random effects of
cohort, pen nested within cohort, pig nested within pen, and sampling nested within pig were
fitted using residual pseudo likelihood in PROC GLIMMIX, SAS 9.3. The total variance was
estimated as follows: Var (Zijkl= var ( Cohort (i)) + var ( pen (j) + var ( pig (k)) + var ( (ijkl)) =
2

Cohort +

2

sample level,

pen +

2

pig +

2

/3. Where

2

/3 is the variance occurring at the individual fecal

2
2
2
pig at the pig level, pen at the pen level, and Cohort at the cohort level.

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RESULTS
Assessing linearity of continuous variables and transformation of variables
Age was the only statistically significant continuous variable that had a linear relationship
with the log odds of Salmonella. For the remaining variables, the following approaches were
taken: as the lowess curves demonstrated a curvilinear shape, a quadratic term was tested for
significance for the following variables: absolute temperature, hourly average temperature,
maximum lagged temperature and temperature humidity index. The hourly variance and
minimum lagged temperature were categorized in quartiles. The following environmental
parameters had a correlation less than 0.8; therefore these variables were included in same
model: 1) average hourly temperature and variance (quartiles); 2) maximum lag temperature and
minimum lag temperature. The cumulative degree and hours above and below the TNZ and the
degree and hours above THI threshold were transformed as described below.
A categorical transformation of cold and heat exposures was conducted. Cold exposure
was defined as any time in each respective lag time that the pen had a temperature below the
lower critical value of TNZ adjusted for pig age (Table 4.1); the reference group was pigs
exposed to temperatures within the TNZ or above the UCT. An identical approach was
conducted for the heat exposure, such that any time the pen had a temperature above the upper
critical value of the TNZ for the respective pig age, it was categorized as heat exposed; for this
variable the reference group was those pigs exposed to temperatures within the TNZ or below
the LCT. Because of the distribution of the data of number of hours and units above THI of 72,
the heat index exposure was transformed into a categorical variable, with heat exposure being

189

exposure to a THI greater than 72 (reference group was pigs exposed to THI less than or equal
72).
Significant risk factors at the cohort level that did not meet the assumption of linearity
were categorized into a binary variable: 1) mortality, morbidity (i.e., total of treatments) and the
proportion of subject pigs that was estimated as a proportion based on total number of pigs
placed at the beginning of each cohort and then categorized in 2 levels depending on central
tendency; less than or equal to the mean (mortality = 1.78%; subject pigs = 1.87%) or median
(morbidity = 0.48%) (reference level) and greater than the mean/median for all cohorts; 2)
nursery Salmonella status was categorized into 2 levels based on the overall mean of the total
positive pools (less than or equal to the mean, 3.64% (reference level) and greater than the
mean); 3) barn environmental status was categorized as positive when at least one sample was
Salmonella positive (reference level, all swabs were negative).

Descriptive Results
Salmonella prevalence results/ isolation of Salmonella
A total of 900 pigs was selected for inclusion in the study, 899 pigs were sampled at least
once for fecal culture. The total loss due to missing sample for fecal collection was 5.1%
(364/7200). Causes of missing sample and detailed description of Salmonella prevalence are
described elsewhere (Pires et al., 2012). Nursery sampling could not be conducted in one of the
cohorts (site A, cohort 3); therefore, the average of the positive pools among the nurseries of the
site was attributed to that cohort in order to be able include the cohort in multilevel analysis.
Salmonella was cultured from 6.6% (453/6836) of individual fecal samples. The distribution of
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Salmonella prevalence by categorical variables at the pig , pen and cohort levels is presented
on Table 4.2.

Descriptive statistics of environmental thermal parameters
Missing observations for the environment pens
Due to a mechanical failure, temperature and humidity were not recorded during the first
visit for cohort 1 at site A. Accordingly, this visit was excluded in all risk factor analyses.
Therefore, the dataset used in model building for risk factor analyses was reduced to 6787
samples with a prevalence of 6.6% (448/6787) (Table 4.2). The risk factor analyses were
conducted for the lag times 12 and 24 hours for all remaining cohorts (17/18) and for 48 and 72
hours in almost all cohorts (16/18) except visit 1 cohort 5, site A (the recorded period time was
less than 72 hours). Since environmental monitoring was not possible prior to pig placement,
risk analyses for the lag times of 1 week and 1 month were conducted for visit 2 and greater and
visit 4 and greater, respectively. Hourly average, maximum lagged and minimum temperature ,
and temperature humidity index within 24h are graphically represented in in Figures 4.1 to 4.12,
and stratified by site, cohort and pig age. Descriptive statistics of the environmental parameter
variables used in the univariable and multivariable models are summarized in Tables 4.3 and 4.4.

191

Risk analyses
The data represent a four level hierarchical structure with cohort at the highest level
(N=18), followed by pen (N = 361), pig (range = 898 899) and individual fecal samples (range =
6412 6751) (Table 4.5.). Note that site (A, B and C) were treated as fixed effect.
The significant explanatory variables in the multivariable models are presented in Table
4.5; a separate model is presented for each significant main thermal exposure (n=5): 1) cold
exposure at 12 hours, 2) 24 hours and 3) 72 hours; 4) heat index exposure at 24 hours and 5) 48
hours. There was a significant association between cold exposure and the odds of Salmonella
shedding; pigs exposed to temperatures below their TNZ were more likely to be Salmonella
positive (OR 1.51, 1.58 and 1.43 for temperatures measuered 12, 24 and 72 hours prior to
sampling, respectively). Likewise, pigs exposed to an excessive heat index (THI > 72) 24 and 48
hours prior to sampling were at higher risk for shedding Salmonella (OR (24h) = 1.46; (95% C.I.
1.03 2.07); OR (48h) =1.45; (95% C.I. 1.01 2.09)). Nursery Salmonella status and cohort
finisher mortality were significant in the final models. Pigs from nurseries with the proportion of
positive pools greater than the mean were more likely to shed Salmonella. Pigs from cohorts
with mortality greater than the mean were more likely to be Salmonella positive. There was also
a significant effect of site; pigs from site A were at greater odds of being Salmonella positive
compared to site C. Age was significantly associated with Salmonella status. As pig age
increased, the risk of Salmonella shedding decreased linearly. For instance, in model 1 the
relative odds of Salmonella shedding decreased 30% (OR (12h) = 0.7; (95% C.I. 0.65 0.74)) for
each 2 week increase in age. No evidence of confounding was found regarding pig health status,
morbidity or season. No significant interaction effects were identified (p –value > 0.05).
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A random slope for pig age was introduced in the final models in order to account for
auto-correlation; however, the models failed to converge with the random slope included in the
models. Different estimation methods (e.g., Laplace approximation and Quad, Gauss-Hermit
quadrature) were tried without success. The Hosmer-Lemeshow goodness of fit test was
assessed on fixed effect models. The test was significant for the 5 models (p value < 0.001).

Variance components
Estimates of variance, standard error of variance and proportion of variance for
Salmonella shedding at each level are presented in Table 4.6. In the multi level intercept only
model, the proportion of variation explained at the sample, pig, pen and cohort levels was 44.8%,
10.2%, 20.5% and 24.5%, respectively. Only one model (model 1) was presented to explain the
proportion of variation as the relative proportion at different levels were identical in all 8 final
models. The proportion of variance explained at sample, pig, pen and cohort levels was 50.8%,
14.8%, 26.5%, 7.9%, respectively (Table 4.6).

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DISCUSSION
To our knowledge, this is the first study to evaluate time dependent environmental risk
factors influencing Salmonella shedding in finishing pigs. Multilevel models with 4 levels
(cohort, pen, pig and individual fecal sample) were used to take into account the hierarchical data
structure and to estimate the contribution of different levels to total variation of Salmonella
shedding.
Sub optimal thermal conditions in the barns were associated with Salmonella shedding
and this association was significant at time periods relatively proximal to fecal sampling (72 h or
less). Those conditions represented extremes of thermal conditions, either exposure to cold or
heat index in a short term time period, which might imply that only extremes have a significant
effect on Salmonella shedding. The short time period prior to sampling being associated with
Salmonella shedding may be a function of a true, short-term effect, and/or may suggest that pigs
could have adapated to these temperatures with more time exposure. A biological explanation
for the association between the thermal environment and Salmonella shedding is that
sub optimal temperature might increase pig stress, which can lead to lowered immunity and
increased susceptibility to new infections and/or recrudescence of shedding in Salmonella
carriers (Funk et al., 2001a; Hald and Andersen, 2001; Smith et al., 2010). The mechanisms
behind the increased risk of infection when pigs are exposed to stress are complex and partially
unknown (Mulder, 1995; Berends et al., 1996; Rostagno, 2009). But stress is generally
considered to suppress the immune system and may lead to an increase in the occurrence of
diseases (Salak-Johnson and McGlone, 2007). These data suggest that thermal environment may

194

at least be a component of this causal pathway and that decreasing the exposure to sub optimal
thermal parameters might decrease Salmonella in swine.
These data do represent the challenges producers face to keep the thermal environment
within the TNZ, in particular during periods of extreme outdoor temperature even though the
buildings have a controlled programmed temperature and ventilation system. The range of the
TNZ thresholds are narrower in young pigs than older pigs (Jacobson et al.; Harmon and
Hongwei, 1995; Fangman and Zulovich, 2000), and as consequence the likelihood for a pig
being outside the TNZ at younger ages is greater than the older ages (end of the finishing
period). The implications are that young pigs are at higher risk of being exposed to temperatures
outside of the TZN ia also associated with the fact that young pigs are more susceptible to cold
temperatures (Young, 1981; Moro et al., 1998; Carroll et al., 2001; Jones et al., 2001) and they
are more prone to infection (Carroll et al., 2001; Jones et al., 2001) it might have a
maginification effect of the both factors combined in the earlier stages of the finishing phase.
The available studies to evaluate heat and cold stress in swine are based mainly on
outcomes such as production, animal behavior, metabolic/physiologic parameters and
reproduction (Bloemhof et al., 2008). Few studies have investigated the relationship between
foodborne pathogens and exposure to temperatures outside of the TNZ. The relationship
between thermal stress and the intestinal microflora of swine has been mainly reported related to
E. coli infections (Moro et al., 1998; Moro et al., 2000; Jones et al., 2001; Mathew et al., 2003).
In these studies, exposure to thermal stress was associated with shifts in the antimicrobial
resistance profile of E. coli isolated from the feces, potentially suggesting a shift in microbial
populations or increases in antimicrobial resistance transfer under sub optimal thermal
conditions. In the present study it is unclear if the association between the sub-optimal thermal
195

exposures and Salmonella is due to a change of gastro intestinal microflora and/or an increased
risk of Salmonella infection or recrudescence of a previous infection; nevertheless, it resulted in
increased shedding in Salmonella during those periods when pigs were outside of the TNZ.
Perharps unexpectedly, an association between Salmonella shedding and the high lagged
temperature or heat load was not found; this is in contrast with reports of human salmonellosis,
which have been associated with high ambient temperatures worldwide (Bentham and Langford,
2001; Kovats et al., 2004; Fleury et al., 2006; Naumova et al., 2007; Lake et al., 2009). On one
hand, the lack of association might instead reflect the involvement of food contamination at other
levels of the food chain; such as, from contamination or during food processing, transport, during
commercial or home preparation of food. On the other hand, the association between heat index
and Salmonella shedding may re enforce the importance of exposure to high temperature and
high relative humidity simultaneously for Salmonella risk in swine. The negative impact of heat
stress on swine performance and health are well described in literature. The impact of heat
abatement on loss of daily gain and death of grow finishing swine when exposed heat stress has
been associated with economic losses in multi state study (St-Pierre et al., 2003).
Cost effectiveness of heat abatement interventions needs to be further investigated regarding
Salmonella risk and production improvement in commercial swine systems. It may be that heat
abatement costs that benefit public health may be economically viable for the producer through
gains in production performance.
Others authors have reported an association between sub optimal temperature and
temperature variability and Salmonella infection in swine (Funk et al., 2001a; Hautekiet et al.,
2008; Smith et al., 2010); however, these were limited by study design and source of thermal
196

data, which often did not represent the exposure inside the barn. The present study evaluated the
effect of environmental parameters on Salmonella shedding by taking into account the
time variant nature of thermal risk factors by monitoring the thermal environment in the barn in
real time, as opposed to those that either considered the temperature retrieved from the closest
weather station (Funk et al 2001; Smith 2010) or based on ventilation control settings above the
TNZ in the barn (Hautekiet et al 2008).
The reference values of TNZ considered in this paper were developed based on
performance and physiological responses to thermal stress and not on susceptibility to infections.
One of the challenges to evaluate the effect of sub optimal thermal parameters is to define the
ideal TNZ range for finishing pigs. There is some divergence among the recommendations for
TNZ (Jacobson et al.; Harmon and Hongwei, 1995; Fangman and Zulovich, 2000). Moreover,
the effective temperature experienced by the pig will be different from air temperature measured
in this study due to several factors such as drafts at the animal level, building insulation, and
floor type (dry versus wet concrete, use of mats for draft protection) (Young, 1981; Gaughan et
al., 2008). Adjustments to air temperature, accounting for drafts, building insulation and floor
type, have been suggested to estimate the effective temperature (Fangman and Zulovich, 2000).
Other factors might have an effect on stress response such as presence of environmental gases in
barn (e.g., ammonia, carbon monoxide or dioxide, hydrogen sulfide) (Jacobson et al.; Fangman
and Zulovich, 2000). Those factors were not taken into account in this study. Nor were adaptive
behavioral changes such as grouping or huddling, which can affect the experienced temperature
(Young, 1981). Pig acclimation to the thermal environment was not evaluated. Pigs can adapt to
thermal changes, and the time interval of adaptation depends on many factors (e.g., time and
magnitude of exposures) (Renaudeau et al., 2008). Pig acclimation to the environmental
197

temperatures may in part explain why only short term exposures were found to be significant in
this study.
Season was not significant in the multivariable models. As previously discussed, there is
significant variability in the literature regarding season and Salmonella shedding on swine (Funk
et al., 2001a; Hald and Andersen, 2001; Hautekiet et al., 2008; Smith et al., 2010). The lack of
consistency in the literature might reflect not only variations of temperature, humidity and
precipitation (Naumova, 2006) but also management factors (Hald and Andersen, 2001).
Therefore, season might be a proxy of the thermal environmental oscillations in the barn, which
could explain the lack of association in this study. This could be due to the fact that the effect of
season can be off set by management of the barn environment, so that actual the variability
reported reflects varying capabilities for producers to keep pigs within the TNZ. Ventilation and
heating of the barns are adjusted in response to the seasonal climatic changes, which might be a
challenge in certain seasons (Funk and Gebreyes, 2004). Therefore, it is possible that some
unobserved factors such as management may affect the seasonal pattern of Salmonella infection
in swine. Moreover, management practices specific to production system type from country to
country can contribute to different of Salmonella seasonality patterns in swine described in
literature. Another justification to the fact season is not significant in this study is that the
variability associated with cohort (either due to seasonal or management differences among
cohorts) is that season may have been accounted for by controlling for cohort as a random effect,
since the season and cohort are related because each cohort occurred over two (17 cohorts) or
three (1 cohort) consecutive seasons.
There was a strong association between a fecal sample being Salmonella positive and
cohorts with positive nursery pools greater than the mean. Pigs entering the finisher from these
198

cohorts were exposed to Salmonella in the nursery and may have been shedding at arrival to the
finishing barn. Because of the exposure during the nursery phase, it is unclear if the effect of
sub optimal thermal environment, in particular cold exposure in young pigs, increases
susceptibility to new infections, and/or the shedding duration during the finishing phase in pigs
that were Salmonella positive at arrival. Nevertheless, the negative impact of cold exposure for
production and health outcomes in young pigs is well described (Young, 1981; Moro et al.,
1998; Carroll et al., 2001; Jones et al., 2001), so improvement of environmental temperature
management in the barn by keeping pigs within the TNZ range may not only decrease
Salmonella shedding, but also will contribute to improve growing pig performance.
Despite the fact that environmental contamination can be a source of Salmonella
infection, exposure to a Salmonella contaminated barn was not significant in the final models.
Pigs exposed to a contaminated environment have been found to be at higher risk for Salmonella
in previous studies (Beloeil et al., 2003; Beloeil et al., 2004; Beloeil et al., 2007). The presence
of residual contamination is related to cleaning and disinfection of the facilities and equipment;
those practices have been inconsistently associated with decrease of Salmonella prevalence on
swine farm (Funk and Gebreyes, 2004; Fosse et al., 2009), either by decreasing the risk
(Hautekiet et al., 2008; Cardinale et al., 2010) or by increasing the risk (van der Wolf et al.,
2001; Poljak et al., 2008), or no difference among different practices (Rajic et al., 2007). One of
the explanations for lack of significance could be due to a non differential exposure among
cohorts by losing information when this risk factor was categorized into a binary variable. In
fact, the majority of the positive fecal samples (85%; 383/453) were from cohorts with at least
one positive environmental swab sample. Since Salmonella is difficult to eliminate from the
barn (Funk and Gebreyes, 2004), and cleaning and disinfection only reduces the contamination
199

pressure (Mannion et al., 2007; Zewde et al., 2009), combined with the use of an imperfect
diagnostic test to isolate Salmonella (Love and Rostagno, 2008), all the cohorts might have
identical exposure regarding Salmonella contamination of the barn.
A decrease in Salmonella risk occurred with increasing pig age. Other authors have also
reported a decrease in prevalence during the finishing period (Kranker et al., 2003; Nollet et al.,
2005; Vigo et al., 2009; Molla et al., 2010).
There was a significant association between cohort mortality and Salmonella shedding.
Salmonella infection in swine is mainly subclinical; mortality associated with clinical cases in
swine are tipically associated with two main serovars (S. Cholerasuis var. Kunzendorf and S.
Typhimurium) (Fedorka-Cray et al., 2000; Barrow et al., 2010). The servovars of the
Salmonella isolates in this study were not identified. No clinical cases of salmonellosis were
reported during the study period. The significant association with mortality for Salmonella
shedding might a result of mortality being a proxy of overall cohort health and/or management
practices. An association between Salmonella status and several swine diseases has been
reported (Møller et al., 1998; van der Wolf et al., 2001; Fablet et al., 2003; Beloeil et al., 2004;
Beloeil et al., 2007). On the other hand, Lo Fo Wong et al., (2004) reported no association
between health status and seroprevalence in European herds (Lo Fo Wong et al., 2004). Despite
the limitation of not knowing the causes of mortality, there was no association with individual
health status at the pig level or cohort morbidity and Salmonella shedding.
A significant difference was observed among sites despite belonging to the same
production system and having an identical pig source, feed and overall management procedures.
Other authors have reported variability in Salmonella prevalence among herds and within the

200

same herd over time (Funk et al., 2001b; Rajic et al., 2005). The observed difference might be
due to unmeasured factors associated with site, such as producer behaviors and biosecurity.
In this study, the variance estimates for Salmonella shedding calculated in both models
(null and full model with significant fixed effects) differed numerically and in relative variance
regarding the cohort and pen levels. The individual fecal sample was the level with highest
variance in both models. Taking into consideration the type of approach used to estimate the
proportion of variation, the use of the latent variable usually attributes the highest variability to
the lowest level (Dohoo et al., 2001; Funk et al., 2007). In the null model, after the sample level,
the next highest relative proportions of variance were at the cohort and pen levels (24.5% and
20.5%, respectively). Organizational levels that explain the greatest amount of variation are
considered the best for targeting interventions (Dohoo et al., 2001; Funk et al., 2007). Based on
these data the cohort appears to be appropriate level to target interventions to reduce Salmonella
shedding. Moreover, the highest variability levels (cohort and pen) should be taken into account
when a sampling scheme is put in practice in epidemiological studies. Comparison of
distribution of sources of variation between the null model and model with significant fixed
effects (model 1) showed a reduction of the overall variance and change in the variance
distribution. The proportion of variance attributable to cohort decreased, which can be explained
by the inclusion of significant fixed effects at the cohort level (mortality and Salmonella positive
pools in nursery). Nevertheless, pen and cohort remained significant sources of variation. Other
authors have reported pen as a significant source of variation as well (Funk et al., 2007), as
opposed to others that identified the highest source of variation as being the farm level (Poljak et
al., 2008). This study differs from those by being a longitudinal study which investigated the
association of time dependent variables at the pen level with repeated sampling within pig. We
201

did not measure previously described pen level risk factors such as pen density (Funk et al.,
2001a; Hautekiet et al., 2008) or pen weight (Poljak et al., 2008). However, those two factors
were not taken into account because the pen-density was kept relatively constant over the study
period and pig weight is highly correlated with pig age. Clustering of Salmonella shedding
within the pen has been described in several studies (Davies et al., 1997; Beloeil et al., 2003;
Funk et al., 2007; Poljak et al., 2008; Rao et al., 2010) and is primarily hypothesized to be a
result of increased risk of transmission among pen mates (Beloeil et al., 2003; Funk et al., 2007;
Rao et al., 2010).
Despite the fact that the majority of variance was associated with individual fecal
samples, the interpretation of the sources of variation and comparison with other studies should
be done carefully because of the latent variable approach (Dohoo et al., 2010c). The estimates of
the variance and respective standard errors calculated using the restricted pseudo likelihood can
lead to bias due to underestimation of the variance and standard errors (Dohoo et al., 2001;
Masaoud and Stryhn, 2010). However, studies comparing methods to estimate model variance
structure (using different estimating algorithms) of random effects have shown different numeric
values, but with same trend in proportional distribution of the variation (Dohoo et al., 2001;
Poljak et al., 2008).
One of limitations of this study is the using only one swine production company. One
unique production company was selected in order to improve internal validity of the study, to
control for potential confounders such as genetics, feed, treatment and vaccination protocols,
biosecurity, and management practices. Moreover, due to the type of study, with monitoring of
environmental thermal parameters in real time, it would be difficult to implement in several
production companies simultaneously. Nevertheless, the selected production system is
202

representative of the swine industry in the US and two types of buildings were included in order
to account for different ventilation systems used in swine barns.
The other limitation is related to study design; because the study began at the time of
placement in the finisher barn (10 weeks of age) there were differences in the duration of lag
times available to be recorded among cohorts, particularly in first visit. Therefore analysis of
thermal environmental effects was restricted to shorter lag times (12 to 72 hours) for the first
collection (10 weeks of age) as well as exclusion of longer lag times from the second visit
(longest recorded lag period was 1 week) and the monthly lag period could not be evaluated until
the 4th collection (16 weeks of age). The reduction of sample size due to no recording of
environmental parameters might be to compromise the ability to find an effect in long term
exposures, not only as a result of reduced power of detection in the older age groups, but also as
a consequence of not being measured in younger pigs. Although a lack of association due to
study limitations cannot be ruled out, previous studies based on effect of thermal stress on
gastro-intestinal pathogen changes have been focused on the effects of short-term exposures
(Moro et al., 1998; Moro et al., 2000; Jones et al., 2001).
Mechanical failure of the sensors resulted in loss of data regarding thermal parameters in
some of the cohorts leading to reduction of the sample size, and consequently power to discern a
difference in the dataset for the temperature parameters. This mechanical failure contributed to a
decrease in sample size of 0.03%

4.8%.

Statistical analysis of binary data, repeated measures and with hierarchical structure is a
challenge (Dohoo et al., 2001; Masaoud and Stryhn, 2010) and some procedures can be
computationally intensive. In order to reduce the unexplained auto correlation of repeated
sampling within pig a time varying variable (age) was included as fixed effect in models and a
203

random slope for age was offered in final models (Masaoud and Stryhn, 2010). Convergence
problems were found when the random slope for age was tested; therefore a simpler model,
without a random slope, was selected. The algorithm used in this study, restricted
pseudo likelihood, under certain conditions might be prone to bias towards the null (Masaoud
and Stryhn, 2010). In simulation models of repeated measures studies, this approximation
method has been shown to perform worse as compared to algorithms, leading to a downward
bias of the estimates (Masaoud and Stryhn, 2010). Despite the possible bias towards to the null,
the findings here reported support that there is a significant association among sub optimal
thermal environment and Salmonella shedding in swine. Discrepancy among the results from
different estimation procedures for binary responses and multilevel data suggests that multiple
procedures should be considered when fitting those models (Dohoo et al., 2001; Masaoud and
Stryhn, 2010). Simultation studies accommodating both the data structure of repeated measures
with binary outcoms and multilevel structures of the data are lacking in order to determine the
best approach to analyze this type of data. A comparative study using this dataset might be useful
to compare the estimates using different statistical approaches and is recommended for future
research.

204

CONCLUSION
Sub-optimal thermal conditions in the barns were associated with Salmonella shedding
and this association was significant at time periods relatively proximal to fecal sampling (72 h or
less). Those sub otimal conditions were extremes, either exposure to cold (temperatures below
the thermal neutral zone) or to heat index value of > 72, which reflects the challenge to keep the
thermal environment within pigs’comfort zone even in mechanically controlled environments
such as swine buildings. Interventions that target the thermal environment may reduce
Salmonella shedding in swine and improve pig well being and production efficiency. These
types of interventions are encouraging, as the production benefits may provide incentive for
producers to use environmental management as an intervention for Salmonella control.
Alternatively, thermal parameters may be used to identify groups of pigs at high risk for
Salmonella shedding. Future studies to identify efficacious and cost effective thermal
environmental interventions are needed.

205

ACKNOWLEDGMENTS
This work was supported by USDA NRI, Epidemiologic Approaches to Food Safety
Grant 2007 01775. The authors thank the participating pork producers and their staff for
collaborating in the investigation, and staff and students at Michigan State University for their
technical support. The authors would like to thank Henrik Stryhn from Atlantic Veterinary
College, UPEI, Joseph Gardiner and Tapabrata Maiti from Michigan State University for
valuable input on statistical analysis.

206

APPENDIX

207

a

Table 4.1. Upper and lower critical temperature criteria of thermal neutral zone of finishing pigs used to assess the thermal (heat and
cold) exposure.
Thermal Neutral Zone (Temperature C)
Pig age (weeks)

Pig weight (kg)

Lag time 12h, 24h, 48h, 72h, 1 week
a

LCT
10
12
14
16
18
20
22
24
a
b
c
d
e

25

UCT

d

b

d

Lag time 1 month
a

LCT

e

27.8
26.7

NA

36

21.1
18.9

47
58
70
85
98
109

16.7
14.4
13.3
12.2
12.2
11.1

26.7
26.7
26.7
26.7
26.7
26.7

NA
16.7
14.4
13.3
12.2
12.2

e

NA

e

Adapted from Harmon and Hongwei, 1995
Lower critical temperature ( C)
Upper critical temperature ( C)
Thermal environment variables lag time 1 week not estimated at 10 week old
Not applicable, thermal environment variables lag time 1 month not estimated at 10 14 week old

208

b

UCT

e

NA

e

NA

e

NA
26.7
26.7
26.7
26.7
26.7

Table 4.2. Proportion of Salmonella positive samples stratified by pig, pen and cohort variables (risk factors) among 6787 individual
pig fecal samples. Multilevel univariable analysis.
Measured
at level
Pig

Variable (level)
Gender
Male
Female
b
Abnormal health status
No
Yes

Total samples
%

Salmonella positive
individual samples (%)

45.68
54.32

2.87
3.73

a

P value
0.36

0.25
93.37
6.63

6.19
0.41

Cohort
c

Nursery
Greater than mean
Less than or equal to mean
d
Environment
Positive
Negative
e
Mortality
Greater than mean
Less than or equal to mean
f
Morbidity
Greater than median
Less than or equal to median
d
Subject
Greater than mean
Less than or equal to mean
Season

<0.001
43.67
56.33

5.53
1.08
0.18

60.17
39.93

5.6
1
0.11

50.27
49.73

4.71
1.89
0.3

44.45
55.55

1.69
4.91
0.12

33.65
66.35

0.74
5.86
0.01
209

Table 4.2. (cont’d)
Measured
Total samples
Salmonella positive
a
P value
at level
Variable (level)
%
individual samples (%)
Spring
16.55
0.47
Summer
36.98
1.12
Fall
17.17
2.21
Winter
28.7
2.8
Pen
0.28
Subject Pen
1.84
0.13
Sick Pen
1.27
0.01
Other Pens
96.89
6.45
Farm
0.35
Site A
33.73
2.09
Site B
32.46
3.64
Farm
Site C
33.81
0.87
a
Univariable analysis, multilevel logistic models with random intercepts at pig , pen and cohort levels
b

Abnormal health status (Yes) when one of the events occurred at the sampling time:1) diarrhea; 2) sick or being moved to the sick
pen; 3) undersized pig; 4) ‘subject’ pig; 5) any sign of disease observed by research personnel
c
nursery Salmonella status: overall mean of the total Salmonella positive pools (reference: less than or equal to the mean 3.64 %)
d

Barn environmental status positive when at least one sample was Salmonella positive

e

Mortality: overall mean of proportion of dead pigs(reference: less than or equal to the median, 1.78%), based on total of pigs placed
at the beginning of each cohort
f
Morbidity: median of proportion of total treatment (reference: less than or equal to the median, 0.48%), based on total of pigs placed
at the beginning of each cohort
g
Subject status: median (reference: less than or equal to the median, 1.87%) of proportion of total pigs that were deined by farm
personnel as abnormal and housed separately, based on total of pigs placed at the beginning of each cohort
h
Type of pen
210

Table 4.3. Descriptive statistics of the continuous thermal environment risk factors, univariable analysis.
Salmonella positive fecal
samples (%)

Salmonella negative fecal
samples
c

Mean

Mean

SD

N

P value

2.54

21.56

2.83

6644

0.004

21.22
21.87
21.9
21.86

2.33
2.52
2.48
2.44

21.37
22.4
22.4
22.38

2.62
3
2.91
2.84

21.77

2.32

22.32

2.87

6743
6746
6698
6698
5919

<0.001
<0.001
<0.001
<0.001
<0.001

21.45

2.18

22.26

2.72

4187

0.63

0.59
2.24
2.58
2.82

0.99
4.53
4.52
5.08

0.7
3.45
4.03
4.51

0.95
5.18
5.2
5.78

3.42

6.13

5.56

5.78

6787
6787
6732
6732
5941

0.73
0.44
0.33
0.94
0.44

4.8

d

Absolute temperature

SD

21.35

Variable

8.06

7.62

15

4182

0.38

22.77
24.2
24.71
25.02

2.69
3.25
3.39
3.44

23.5
25.37
26.06
26.6

3.23
3.96
4.04
4.18

25.7

3.55

27.57

4.51

6719
6745
6690
6690
5887

0.02
0.02
0.06
0.7
0.24

26.56

3.39

29.34

4.4

4024

0.11

d

Hourly average temperature
12 hours
24 hours
48 hours
72 hours
a
1 week
1 month

b

Hourly variation temperature
12 hours
24 hours
48 hours
72 hours
a
1 week
1 month

d

b
d

Maximum lagged temperature
12 hours
24 hours
48 hours
72 hours
a
1 week
1 month

b

211

Table 4.3. (cont’d)
Salmonella positive fecal
samples (%)

Salmonella negative fecal
samples
c

Mean

Variable

SD

Mean

SD

N

P value

19.96
19.64
19.07
18.73

2.35
2.48
2.71
2.68

19.94
19.61
18.99
18.66

2.75
2.93
2.95
2.94

17.81

2.4

17.81

2.92

6717
6719
6673
6673
5891

<0.001
<0.001
<0.001
<0.001
0.002

15.82

2.69

16.09

3.16

4085

0.002

68.31
67.67
67.68
67.64

6.09
5.89
5.9
5.71

69.82
68.71
68.7
68.71

6.38
5.77
5.71
5.52

67.18

5.84

68.61

5.6

6617
6622
6572
6577
5931

0.001
0.003
<0.001
<0.001
0.001

66.33

4.75

68.51

4.93

4187

0.9

d

Minimum lagged temperature
12 hours
24 hours
48 hours
72 hours
a
1 week
b

1 month
Temperature Humidity Index
12 hours
24 hours
48 hours
72 hours
a
1 week
1 month

b

a

Estimates of collections 2 and greater

b

Estimated of collections 4 and greater

c

Univariable analysis, multilevel logistic models with random intercepts at pig , pen and cohort levels

d

Temperature units C

212

Table 4.4. Descriptive statistics of the categorical thermal environment risk factors, univariable analysis.
Variable (level)
Hourly variation 12 hours
Q1
Q2
Q3
Q4
Hourly variation 24 hours
Q1
Q2
Q3
Q4
Hourly variation 48 hours
Q1
Q2
Q3
Q4
Hourly variation 72 hours
Q1
Q2
Q3
Q4
Hourly variation 1 week
Q1
Q2
Q3
Q4

Total samples
(%)

Salmonella positive
individual samples (%)

22.32
24.72
27.89
25.06

1.5
1.92
2.09
1.09

N

a

P value
0.03

6787

0.7
6787

24.85
25.12
24.87
25.05

2.31
2.03
1.49
0.77
6732

2.63
1.93
1.14
0.88

0.71

0.53

2.3
2.24
1.17
0.86

23.14
26.62
25.16
25.07

6732

5941

24.85
25.03
24.99
25.13

0.46

24.34
24.91
25.58
25.16

2.49
1.6
0.98
0.62
213

Table 4.4. (cont’d)
Variable (level)

Total samples
(%)

Hourly variation 1 month
Q1
Q2
Q3
Q4
Lowest lagged temperature 12 hours
Q1
Q2
Q3
Q4
Lowest lagged temperature 24 hours
Q1
Q2
Q3
Q4
Lowest lagged temperature 48 hours
Q1
Q2
Q3
Q4
Lowest lagged temperature 72 h
Q1
Q2
Q3
Q4

Salmonella positive
individual samples (%)

N
4182

31.35
25.32
17.74
25.59

a

P value
0.25

2.13
1.03
0.45
0.38
6719

6719

<0.001

6673

<0.001

6673

20.93
24.1
25.35
25.63

0.005

<0.001

1.52
1.85
1.83
1.38

24.93
24.1
25.35
25.63

1.52
1.85
1.83
1.38

24.55
25.27
24.79
25.4

1.38
2.05
1.74
1.38

24.94
24.64
25.36
25.07

1.41
1.84
1.84
1.45
214

Table 4.4. (cont’d)
Total samples
(%)
Lowest lagged temperature 1 week
Q1
24.49
Variable (level)

Q2
Q3
Q4
Lowest lagged temperature 1 m
Q1
Q2
Q3
Q4
Cold exposure 12 hours
Yes
No
Cold exposure 24 hours
Yes
No
Cold exposure 48 hours
Yes
No
Cold exposure 72 hours
Yes
No
Cold exposure 1 week
Yes
No

Salmonella positive
individual samples (%)

a

0.96

0.001

0.001

6701

11.6
88.4

0.08

6751

24.77
23.53
26.02
25.68

5891

6751

24.7
25.41

P value

4085

25.39

N

0.001

1.37
1.78
1.41
1.1
0.91
1.49
1.03
0.54
1.39
5.18

13.15
88.65

1.48
5.1

17.04
82.96

1.9
5.61
0.001

18.13
81.87

1.97
4.58
5920

14.9
85.1

1.33
4.32
215

0.001

Table 4.4. (cont’d)
Variable (level)
Cold exposure 1 month
Yes
No
Heat exposure 12 hours
Yes
No
Heat exposure 24 hours
Yes
No
Heat exposure 48 hours
Yes
No
Heat exposure 72 hours
Yes
No
Heat exposure 1 week
Yes
No
Heat exposure 1 month
Yes
No
Heat Index exposure 12 hours
Yes
No

Total samples
(%)

Salmonella positive
individual samples (%)

N
4160

21.63
78.37

0.04

1.08
2.88

13.96
86.04

a

P value

0.27
6.34

6782

6782

0.13

6732

0.46

5941

0.99

4182

0.23

6462

39.81
60.19

0.08

6732

32.88
67.18

0.1

<0.001

1.28
5.32
1.56
5.02

43.52
56.48

1.62
4.96

51
49

1.48
4.21

60.9
39.1
38.19
61.81

1.12
2.87
1.92
4.49

216

Table 4.4. (cont’d)
Variable (level)

Total samples
(%)

Salmonella positive
individual samples (%)

51.32
48.68

2.29
3.92

57.72
42.28

2.81
3.57

Heat Index exposure 24 hours
Yes
No
Heat Index exposure 48 hours
Yes
No
Heat Index exposure 72h
Yes
No
Heat Index exposure 1 week
Yes
No
Heat Index exposure 1 month
Yes
No
a

N
6462

a

P value
<0.001

<0.001
6412
6412
64.19
35.81

3.01
3.37
5668

70.17
29.83

0.05

2.86
2.61
3908

85.44
14.56

0.002

0.39

2.87
0.69

Univariable analysis, multilevel logistic models with random intercepts at pig , pen and cohort levels

217

Table 4.5. Final multivariable random effects logistic regression models of associations between thermal environment parameters,
pig level and cohort level risk factors and Salmonella shedding in finishing pigs in three sites.
a

b

Independent variable

Beta

SE

OR

Intercept
Cold exposure 12 hours
f
Nursery status

Models
Model 1

-3.39
0.41

0.64
0.2

1.93

c

d

e

Mortality
h

Age

P-value

…
1.51

…
1.02-2.25

…
0.04

0.46

6.91

2.79-17.15

<0.001

1.08

0.48

2.95

1.15-7.55

0.02

-1.181

g

95% CI

0.017

0.7

0.65-0.74

<0.001

i

Site
0.01
A vs C
0.7
0.58
2.01
0.65-6.21
B vs C
1.8
0.61
6.06
1.84-19.98
cohorts (n=18); pens (n=361); pigs (n=899); individual fecal samples (n=6751); Salmonella prevalence
(6.58%)
Model 2
Intercept
Cold exposure 24 hours
f
Nursery status
g

Mortality
h

Age

-3.42
0.45

0.64
0.19

…
1.58

…
(1.07-2.30)

…
0.02

1.93

0.47

6.92

(2.77-17.31)

<0.001

1.08

0.48

2.94

(1.14-7.59)

0.02

-0.18

0.017

0.7

(0.65-0.74)

<0.001

i

Site
0.01
A vs C
0.7
0.58
2.02
(0.65-6.29)
B vs C
1.81
0.61
6.12
(1.84-20.37)
cohorts (n=18); pens (n=361); pigs (n=899); individual fecal samples (n=6751); Salmonella prevalence
(6.54%)
218

Table 4.5. (cont’d)
a

b

c

d

e

Independent variable

Beta

SE

OR

Intercept
Cold exposure 72hours
f
Nursery status

Models
Model 3

-3.44
0.36

0.65
0.18

…
1.43

2

0.47

7

1.04

0.48

7.35

(2.93-18.42)

0.03

-0.18

0.018

0.7

(0.65-0.75)

<0.001

g

Mortality
h

Age

95% CI

P-value

…
(1-2.04)

…
0.05
<0.001

i

Site
0.01
A vs C
0.58
0.58
1.78
(0.57-5.57)
B vs C
1.76
0.61
5.81
(1.77-19.13)
cohorts (n=18); pens (n=361); pigs (n=898); individual fecal samples (n=6701); Salmonella prevalence
(6.55%)
Model 4
Intercept
Heat Index exposure 24 hours
f
Nursery status
g

Mortality
h

Age

-3.68
0.38

0.65
0.18

…
1.46

…
(1.03-2.07)

…
0.032

1.96

0.44

7.14

(2.99-16.95)

<0.001

1.24

0.46

3.47

(1.41-8.54)

0.007

-0.18

0.017

0.7

(0.65-0.74)

<0.001

i

Site
A vs C
B vs C

0.006
0.65
1.81

0.55
0.59

1.92
6.09

(0.65-5.65)
(1.92-19.30)

cohorts (n=18); pens (n=361); pigs (n=899); individual fecal samples (n=6462); Salmonella prevalence
(6.41%)
219

Table 4.5. (cont’d)
a

b

Independent variable

Beta

SE

OR

Intercept
Heat Index exposure 48 hours
f
Nursery status

Models
Model 5

-3.61
0.37

0.64
0.19

2.02

g

Mortality
h

Age

c

d

e

95% CI

P-value

1.45

(1.01-2.09)

0.046

0.44

7.54

(3.18-17.91)

<0.001

1.22

0.46

3.4

(1.39-8.33)

0.007

-0.18

0.017

0.7

(0.65-0.74)

<0.001

i

Site
0.005
A vs C
0.54
0.55
1.75
(0.59-5.05)
B vs C
1.8
0.58
7.54
(3.18-17.91)
cohorts (n=18); pens (n=361); pigs (n=898); individual fecal samples (n=6412); Salmonella prevalence
(6.38%)
a

Regression coefficient

b

Standard error of the mean

c

Odds ratio

d
e
f

95% confidence interval
Wald test
Reference less than or equal to mean ( %)

g

Reference less than or equal to mean ( %)

h
i
j

Age 2 weeks unit

Reference: site C
Thermal neutral zone
220

Table 4.6. Variance components and proportion of variance at the cohort , pen , pig and individual fecal level of the null model and
final model (model 1, cold exposure at 12 hours).
Data hierarchy

Cohort
Pen
Pig
b
Individual fecal sample
Total variance
a
Standard error
b

Null Model

Final model Model 1
a

Variance estimate
1.8
1.51
0.75

Se
0.75
0.3
0.18

3.29
7.35

…
…

Individual fecal sample variance:

Proportion (%)
24.5
20.5
10.2
44.8
100

2

/3=3.29 (latent variable technique)

221

a

Variance estimate
0.51
1.72
0.96

Se
0.35
0.35
0.21

3.29
6.48

…
…

Proportion (%)
7.9
26.5
14.8
50.8
100

Figure 4.1.a. Box plot of the average hourly pen temperature ( C) within 24 hours for cohort 1
(AC1, 06/3/2008 – 09/06/2008) and 2 (AC210/08/2008 – 01/24/2009) by pig age in site A. Note
no data on first visit of the cohort 1 (10 week old).

Figure 4.1.b. Box plot of the average hourly pen temperature ( C) within 24 hours for cohort 3
(AC3, 03/21/2009 – 06/29/2009) and 4 (AC4, 08/10/2009 – 11/17/2009) by pig age in site A
222

Figure 4.1.c. Box plot of the average hourly pen temperature ( C) within 24 hours for cohort 5
(AC5, 12/16/2009 – 03/22/2010) and 6 (AC6, 04/24/2010 – 08/02/2010) by pig age in site A.

Figure 4.2.a. Box plot of the average hourly pen temperature ( C) within 24 hours for cohort 1
(BC1, 07/07/2008 – 10/11/2008) and 2 (BC2, 11/22/2008 – 02/28/2009) by pig age in site B.
223

Figure 4.2.b. Box plot of the average hourly pen temperature ( C) within 24 hours for cohort 3
(BC3, 04/25/2009 –08/03/2009) and 4 (BC4, 09/15/2009 – 12/08/2009) by pig age in site B.
Note no data on last visit of the cohort 4 (24week old).

Figure 4.2.c. Box plot of the average hourly pen temperature ( C) within 24 hours for cohort 5
(BC5, 01/11/2010 – 04/17/2010) and 6 (BC6, 05/19/2010 – 08/23/2010) by pig age in site B.
224

Figure 4.3.a. Box plot of the average hourly pen temperature ( C) within 24 hours for cohort 1
(CC1, 06/02/2009 – 09/08/2009) and 2 (CC2, 12/21/2009 – 03/27/2010) by pig age in site C.

Figure 4.3.b. Box plot of the average hourly pen temperature ( C) within 24 hours for cohort
3(CC3, 06/07/2010 – 09/11/2010) and 4 (CC4, 11/20/2010 – 02/26/2010) by pig age in site C.
225

Figure 4.3.c.: Box plot of the average hourly pen temperature ( C) within 24 hours for cohort 5
(CC5, 11/27/2010 – 03/05/2011) and 6 (CC6, 04/30/2011 – 08/08/2011) by pig age in site C.

Figure 4.4.a. Box plot of the maximum lagged pen temperature ( C) within 24 hours for cohort
1 (AC1, 06/3/2008 – 09/06/2008) and 2 (AC2,10/08/2008 – 01/24/2009) by pig age in site A.
Note no data on first visit of the cohort 1 (10 week old).
226

Figure 4.4.b. Box plot of the maximum lagged pen temperature ( C) within 24 hours for cohort
3 (AC3, 03/21/2009 – 06/29/2009) and 4 (AC4, 08/10/2009 – 11/17/2009) by pig age in site A.

Figure 4.4.c.: Box plot of the maximum lagged pen temperature ( C) within 24 hours for cohort
5 (AC5, 12/16/2009 – 03/22/2010) and 6 (AC6, 04/24/2010 – 08/02/2010) by pig age in site A.

227

Figure 4.5.a. Box plot of the maximum lagged pen temperature ( C) within 24 hours for cohort
1 (BC1, 07/07/2008 – 10/11/2008) and 2 (BC2, 11/22/2008 – 02/28/2009) by pig age in site B.

Figure 4.5.b.: Box plot of the average hourly pen temperature ( C) within 24 hours for cohort 3
(BC3, 04/25/2009 –08/03/2009) and 4 (BC4, 09/15/2009 – 12/08/2009) by pig age in site B.
Note no data on last visit of the cohort 4 (24week old).
228

Figure 4.5.c. Box plot of the maximum lagged pen temperature ( C) within 24 hours for cohort
5 (BC5, 01/11/2010 – 04/17/2010) and 6 (BC6, 05/19/2010 – 08/23/2010) by pig age in site B.

Figure 4.6.a.: Box plot of the maximum lagged pen temperature ( C) within 24 hours for cohort
1 (CC1, 06/02/2009 – 09/08/2009) and 2 (CC2, 12/21/2009 – 03/27/2010) by pig age in site C.
229

Figure 4.6.b. Box plot of the maximum lagged pen temperature ( C) within 24 hours for cohort
3(CC3, 06/07/2010 – 09/11/2010) and 4 (CC4, 11/20/2010 – 02/26/2010) by pig age in site C.

Figure 4.6.c. Box plot of the maximum lagged pen temperature ( C) within 24 hours for cohort
5 (CC5, 11/27/2010 – 03/05/2011) and 6 (CC6, 04/30/2011 – 08/08/2011) by pig age in site C.
230

Figure 4.7.a. Box plot of the minimum lagged pen temperature ( C) within 24 hours for cohort 1
(AC1, 06/3/2008 – 09/06/2008) and 2 (AC2, 10/08/2008 – 01/24/2009) by pig age in site A.
Note no data on first visit of the cohort 1 (10 week old).

Figure 4.7.b. Box plot of the minimum lagged pen temperature ( C) within 24 hours for cohort
3 (AC3, 03/21/2009 – 06/29/2009) and 4 (AC4, 08/10/2009 – 11/17/2009) by pig age in site A.
231

Figure 4.7.c. Box plot of the minimum lagged pen temperature ( C) within 24 hours for cohort 5
(AC5, 12/16/2009 – 03/22/2010) and 6 (AC6, 04/24/2010 – 08/02/2010) by pig age in site A.

Figure 4.8.a. Box plot of the minimum lagged pen temperature ( C) within 24 hours for cohort 1
(BC1, 07/07/2008 – 10/11/2008) and 2 (BC2, 11/22/2008 – 02/28/2009) by pig age in site B.
232

Figure 4.8.b. Box plot of the minimum lagged pen temperature ( C) within 24 hours for cohort
3 (BC3, 04/25/2009 –08/03/2009) and 4 (BC4, 09/15/2009 – 12/08/2009) by pig age in site B.
Note no data on last visit of the cohort 4 (24week old).

Figure 4.8.c. Box plot of the minimum lagged pen temperature ( C) within 24 hours for cohort 5
(BC5, 01/11/2010 – 04/17/2010) and 6 (BC6, 05/19/2010 – 08/23/2010) by pig age in site B.
233

Figure 4.9.a. Box plot of the minimum lagged pen temperature ( C) within 24 hours for cohort 1
(CC1, 06/02/2009 – 09/08/2009) and 2 (CC2, 12/21/2009 – 03/27/2010) by pig age in site C.

Figure 4.9.b. Box plot of the minimum lagged pen temperature ( C) within 24 hours for cohort
3(CC3, 06/07/2010 – 09/11/2010) and 4 (CC4, 11/20/2010 – 02/26/2010) by pig age in site C.
234

Figure 4.9.c. Box plot of the minimum lagged pen temperature ( C) within 24 hours for cohort 5
(CC5, 11/27/2010 – 03/05/2011) and 6 (CC6, 04/30/2011 – 08/08/2011) by pig age in site C.

Figure 4.10.a. Box plot of the pen temperature temperature humidity (THI) index within 24
hours for cohort 1 (AC1, 06/3/2008 – 09/06/2008) and 2 (AC2,10/08/2008 – 01/24/2009) by pig
age in site A. Note no data on first visit of the cohort 1 (10 week old).
235

Figure 4.10.b. Box plot of the pen temperature humidity index (THI) within 24 hours for cohort
3 (AC3, 03/21/2009 – 06/29/2009) and 4 (AC4, 08/10/2009 – 11/17/2009) by pig age in site A.

Figure 4.10.c. Box plot of the pen temperature humidity index (THI) within 24 hours for cohort
5 (AC5, 12/16/2009 – 03/22/2010) and 6 (AC6, 04/24/2010 – 08/02/2010) by pig age in site A.
236

Figure 4.11.a. Box plot of the pen temperature humidity index (THI) 24 hours for cohort 1
(BC1, 07/07/2008 – 10/11/2008) and 2 (BC2, 11/22/2008 – 02/28/2009) by pig age in site B.

Figure 4.11.b. Box plot of the pen temperature humidity index (THI) within 24 hours for cohort
3 (BC3, 04/25/2009 –08/03/2009) and 4 (BC4, 09/15/2009 – 12/08/2009) by pig age in site B.
Note no data on last visit of the cohort 4 (24week-old).
237

Figure 4.11.c. Box plot of the pen temperature humidity index (THI) within 24 hours for cohort
5 (BC5, 01/11/2010 – 04/17/2010) and 6 (BC6, 05/19/2010 – 08/23/2010) by pig age in site B.

Figure 4.12.a. Box plot of the pen temperature humidity index (THI) within 24 hours for cohort
1 (CC1, 06/02/2009 – 09/08/2009) and 2 (CC2, 12/21/2009 – 03/27/2010) by pig age in site C.
238

Figure 4.12.b. Box plot of pen temperature humidity index (THI) within 24 hours for cohort
3(CC3, 06/07/2010 – 09/11/2010) and 4 (CC4, 11/20/2010 – 02/26/2010) by pig age in site C.

Figure 4.12.c. Box plot of the pen temperature humidity index (THI) within 24 hours for cohort
5 (CC5, 11/27/2010 – 03/05/2011) and 6 (CC6, 04/30/2011 – 08/08/2011) by pig age in site C.
239

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